
# Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
from scipy.stats import ttest_ind


# Load the data
all_district = pd.read_csv("data_csvs/all_by_district.csv")
girls_district = pd.read_csv("data_csvs/girls_by_district.csv")
all_state = pd.read_csv("data_csvs/all_by_state.csv")
girls_state = pd.read_csv("data_csvs/girls_by_state.csv")
district_dict = pd.read_csv("data_csvs/district_dict.csv")
state_dict = pd.read_csv("data_csvs/state_dict.csv")


all_district.head()


all_district.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 156 entries, 0 to 155
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Season_Start  156 non-null    int64 
 1   District      156 non-null    object
 2   All_Ages      156 non-null    int64 
 3   19&over       156 non-null    int64 
 4   17-18         156 non-null    int64 
 5   15-16         156 non-null    int64 
 6   13-14         156 non-null    int64 
 7   12-Nov        156 non-null    int64 
 8   10-Sep        156 non-null    int64 
 9   8-Jul         156 non-null    int64 
 10  6&U           156 non-null    int64 
dtypes: int64(10), object(1)
memory usage: 13.5+ KB


girls_district.head()


girls_district.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 156 entries, 0 to 155
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Season_Start  156 non-null    int64 
 1   District      156 non-null    object
 2   All_Ages      156 non-null    int64 
 3   19&over       156 non-null    int64 
 4   17-18         156 non-null    int64 
 5   15-16         156 non-null    int64 
 6   13-14         156 non-null    int64 
 7   12-Nov        156 non-null    int64 
 8   10-Sep        156 non-null    int64 
 9   8-Jul         156 non-null    int64 
 10  6&U           156 non-null    int64 
dtypes: int64(10), object(1)
memory usage: 13.5+ KB


all_state.head()


all_state.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 636 entries, 0 to 635
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Season_Start  636 non-null    int64 
 1   District      636 non-null    object
 2   State         636 non-null    object
 3   All_Ages      636 non-null    int64 
 4   19&over       636 non-null    int64 
 5   17-18         636 non-null    int64 
 6   15-16         636 non-null    int64 
 7   13-14         636 non-null    int64 
 8   12-Nov        636 non-null    int64 
 9   10-Sep        636 non-null    int64 
 10  8-Jul         636 non-null    int64 
 11  6&U           636 non-null    int64 
dtypes: int64(10), object(2)
memory usage: 59.8+ KB


girls_state.head()


girls_state.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 636 entries, 0 to 635
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Season_Start  636 non-null    int64 
 1   District      636 non-null    object
 2   State         636 non-null    object
 3   All_Ages      636 non-null    int64 
 4   19&over       636 non-null    int64 
 5   17-18         636 non-null    int64 
 6   15-16         636 non-null    int64 
 7   13-14         636 non-null    int64 
 8   12-Nov        636 non-null    int64 
 9   10-Sep        636 non-null    int64 
 10  8-Jul         636 non-null    int64 
 11  6&U           636 non-null    int64 
dtypes: int64(10), object(2)
memory usage: 59.8+ KB


district_dict.head(13)


district_dict.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 13 entries, 0 to 12
Data columns (total 2 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   District       13 non-null     object
 1   District_Code  13 non-null     int64 
dtypes: int64(1), object(1)
memory usage: 340.0+ bytes


state_dict.head()


state_dict.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 53 entries, 0 to 52
Data columns (total 2 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   State       53 non-null     object 
 1   State_Code  53 non-null     float64
dtypes: float64(1), object(1)
memory usage: 980.0+ bytes


def rename_columns(df):
    """
    Rename specified columns in a DataFrame.

    Args:
        df: Pandas DataFrame
        rename_dict: Dictionary containing old column names as keys and new column names as values

    Returns:
        DataFrame with specified columns renamed
    """
    columns_to_rename = {'12-Nov': '11-12', '10-Sep': '9-10', '8-Jul': '7-8'}
    df.rename(columns=columns_to_rename, inplace=True)
    return df

# Example usage:
# Assuming df is your DataFrame
#df = rename_columns(df)


all_district_ = rename_columns(all_district)
girls_district = rename_columns(girls_district)
all_state = rename_columns(all_state)
girls_state = rename_columns(girls_state)


all_district.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 156 entries, 0 to 155
Data columns (total 11 columns):
 #   Column        Non-Null Count  Dtype 
---  ------        --------------  ----- 
 0   Season_Start  156 non-null    int64 
 1   District      156 non-null    object
 2   All_Ages      156 non-null    int64 
 3   19&over       156 non-null    int64 
 4   17-18         156 non-null    int64 
 5   15-16         156 non-null    int64 
 6   13-14         156 non-null    int64 
 7   11-12         156 non-null    int64 
 8   9-10          156 non-null    int64 
 9   7-8           156 non-null    int64 
 10  6&U           156 non-null    int64 
dtypes: int64(10), object(1)
memory usage: 13.5+ KB


def convert_columns_to_float(df):
    """
    Convert specified columns in a DataFrame to float64 type.

    Args:
        df: Pandas DataFrame
        columns: List of column names to convert to float64 type

    Returns:
        DataFrame with specified columns converted to float64 type
    """
    # Define numeric columns
    numeric_columns = ['Season_Start','All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
    
    # Convert the selected columns to numeric
    df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce').astype('float64')
    return df

# Example usage:
# Assuming df is your DataFrame and numeric_columns is the list of columns to convert
#df = convert_columns_to_float(df)


all_district = convert_columns_to_float(all_district)
girls_district = convert_columns_to_float(girls_district)
all_state = convert_columns_to_float(all_state)
girls_state = convert_columns_to_float(girls_state)


all_state.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 636 entries, 0 to 635
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Season_Start  636 non-null    float64
 1   District      636 non-null    object 
 2   State         636 non-null    object 
 3   All_Ages      636 non-null    float64
 4   19&over       636 non-null    float64
 5   17-18         636 non-null    float64
 6   15-16         636 non-null    float64
 7   13-14         636 non-null    float64
 8   11-12         636 non-null    float64
 9   9-10          636 non-null    float64
 10  7-8           636 non-null    float64
 11  6&U           636 non-null    float64
dtypes: float64(10), object(2)
memory usage: 59.8+ KB


girls_state.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 636 entries, 0 to 635
Data columns (total 12 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Season_Start  636 non-null    float64
 1   District      636 non-null    object 
 2   State         636 non-null    object 
 3   All_Ages      636 non-null    float64
 4   19&over       636 non-null    float64
 5   17-18         636 non-null    float64
 6   15-16         636 non-null    float64
 7   13-14         636 non-null    float64
 8   11-12         636 non-null    float64
 9   9-10          636 non-null    float64
 10  7-8           636 non-null    float64
 11  6&U           636 non-null    float64
dtypes: float64(10), object(2)
memory usage: 59.8+ KB


# Replace values in 'District' column
all_district['District'] = all_district['District'].map(district_dict.set_index('District')['District_Code'])
girls_district['District'] = girls_district['District'].map(district_dict.set_index('District')['District_Code'])
all_state['District'] = all_state['District'].map(district_dict.set_index('District')['District_Code'])
girls_state['District'] = girls_state['District'].map(district_dict.set_index('District')['District_Code'])


# Replace values in 'State' column
all_state['State'] = all_state['State'].map(state_dict.set_index('State')['State_Code'])
girls_state['State'] = girls_state['State'].map(state_dict.set_index('State')['State_Code'])


all_district.head(13)


all_state.tail()


# Selecting relevant columns from all_district and girls_district
all_district_subset = all_district[['Season_Start', 'District', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
girls_district_subset = girls_district[['Season_Start', 'District', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]

# Subtracting girls_district values from all_district values
boys_district = all_district_subset.copy()
boys_district[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']] -= girls_district_subset[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]

# Displaying the resulting DataFrame (boys_df)
boys_district.head()


# Selecting relevant columns from all_district and girls_district
all_state_subset = all_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
girls_state_subset = girls_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]

# Subtracting girls_district values from all_district values
boys_state = all_state_subset.copy()
boys_state[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']] -= girls_state_subset[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]

# Displaying the resulting DataFrame (boys_df)
boys_state.head()


boys_district.describe()


boys_district.describe()


boys_state.describe()


girls_state.describe()


def calculate_season_to_season_growth(df):
    """
    Calculates season-to-season growth for each district in the given DataFrame.

    Parameters:
    - df: DataFrame with columns 'Season_Start', 'District', and numeric columns for age groups.

    Returns:
    - growth_df: DataFrame with season-to-season growth for each district including the seasons being compared.
    """
    # Sort DataFrame by 'Season_Start' for chronological order
    df = df.sort_values(by='Season_Start')

    # Group by 'District' and calculate growth
    growth_df = pd.DataFrame()
    for district, group_df in df.groupby('District'):
        # Calculate growth for numeric columns
        growth = group_df.set_index('Season_Start').pct_change().dropna() * 100
        growth['District'] = district
        growth.reset_index(inplace=True)

        # Add columns for the seasons being compared
        growth['Season_1_Compared'] = growth['Season_Start'].shift(1)
        growth['Season_2_Compared'] = growth['Season_Start']
        
        growth_df = pd.concat([growth_df, growth], ignore_index=True)

    return growth_df


boys_district_growth = calculate_season_to_season_growth(boys_district)
girls_district_growth = calculate_season_to_season_growth(girls_district)


boys_district_growth.head()


girls_district_growth.head()


boys_district_growth['Season_1_Compared'].fillna(2011, inplace=True)
girls_district_growth['Season_1_Compared'].fillna(2011, inplace=True)


boys_district_growth.head(13)


girls_district_growth.head(13)


def calculate_district_growth(df, district_id):
    # Filter data for the specified district
    district_data = df[df['District'] == district_id].copy()

    # Extract data for the 2011 and 2022 seasons
    season_2011 = district_data[district_data['Season_Start'] == 2011]
    season_2022 = district_data[district_data['Season_Start'] == 2022]

    # Calculate growth for each age group
    growth = (season_2022.iloc[:, 2:] - season_2011.iloc[:, 2:]) / season_2011.iloc[:, 2:] * 100

    # Add columns for start and end seasons
    growth['Season_1'] = season_2011['Season_Start'].values[0]
    growth['Season_2'] = season_2022['Season_Start'].values[0]

    # Add a row for overall growth
    overall_growth = pd.DataFrame({
        'District': [district_id],
        'All_Ages': [growth['All_Ages'].mean()],
        '19&over': [growth['19&over'].mean()],
        '17-18': [growth['17-18'].mean()],
        '15-16': [growth['15-16'].mean()],
        '13-14': [growth['13-14'].mean()],
        '11-12': [growth['11-12'].mean()],
        '9-10': [growth['9-10'].mean()],
        '7-8': [growth['7-8'].mean()],
        '6&U': [growth['6&U'].mean()],
        'Season_1': [season_2011['Season_Start'].values[0]],
        'Season_2': [season_2022['Season_Start'].values[0]]
    })

    # Concatenate the individual age group growth and overall growth
    result = pd.concat([growth, overall_growth], ignore_index=True)

    return result

# Example usage:
# district_id = 1  # Replace with the desired district ID
# district_growth = calculate_district_growth(all_district, district_id)
# print(district_growth)


growth_boys_district_13 = boys_district[(boys_district['District'] == 13) & ((boys_district['Season_Start'] == 2022) | (boys_district['Season_Start'] == 2011))]
growth_boys_district_13 = growth_boys_district_13.set_index('Season_Start').diff().iloc[-1]
growth_boys_district_13 = growth_boys_district_13 / boys_district[all_district['Season_Start'] == 2011].set_index('Season_Start').iloc[0] * 100
print(growth_boys_district_13)

District      0.000000
All_Ages     61.228401
19&over      90.785908
17-18        12.939245
15-16        77.337848
13-14        39.321511
11-12       -17.495874
9-10         31.323283
7-8         111.386322
6&U         176.025918
dtype: float64


growth_girls_district_13 = girls_district[(boys_district['District'] == 13) & ((girls_district['Season_Start'] == 2022) | (girls_district['Season_Start'] == 2011))]
growth_girls_district_13 = growth_girls_district_13.set_index('Season_Start').diff().iloc[-1]
growth_girls_district_13 = growth_girls_district_13 / girls_district[all_district['Season_Start'] == 2011].set_index('Season_Start').iloc[0] * 100
print(growth_girls_district_13)

District       0.000000
All_Ages     881.938959
19&over      318.750000
17-18        506.849315
15-16        661.639344
13-14        782.248521
11-12        768.533333
9-10        1218.691589
7-8         2194.090909
6&U         3436.220472
dtype: float64


# Assuming boys_district_growth and girls_district_growth DataFrames
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

for age_group in age_groups:
    boys_growth = boys_district_growth[age_group].dropna()
    girls_growth = girls_district_growth[age_group].dropna()

    # Summary Statistics
    boys_summary = boys_growth.describe()
    girls_summary = girls_growth.describe()

    print(f"Summary Statistics for {age_group} - Boys:")
    print(boys_summary)
    
    print(f"\nSummary Statistics for {age_group} - Girls:")
    print(girls_summary)

    # T-test
    t_statistic, p_value = stats.ttest_ind(boys_growth, girls_growth, equal_var=False)
    
    print(f"\nT-test results for {age_group}:")
    print(f"T-statistic: {t_statistic}")
    print(f"P-value: {p_value}")

    # Check if the difference is significant at a 95% confidence level
    alpha = 0.05
    if p_value < alpha:
        print("\nThe difference is statistically significant.\n")
    else:
        print("\nThe difference is not statistically significant.\n")

Summary Statistics for All_Ages - Boys:
count    143.000000
mean       0.951232
std       10.696090
min      -47.331176
25%       -1.032265
50%        0.864688
75%        2.906340
max       78.580153
Name: All_Ages, dtype: float64

Summary Statistics for All_Ages - Girls:
count    143.000000
mean       3.831219
std       11.687997
min      -42.140241
25%        0.671382
50%        3.400608
75%        6.613878
max       75.621191
Name: All_Ages, dtype: float64

T-test results for All_Ages:
T-statistic: -2.173742633617583
P-value: 0.030556936933928032

The difference is statistically significant.

Summary Statistics for 19&over - Boys:
count    143.000000
mean       2.222269
std       20.469001
min      -61.725510
25%       -1.722565
50%        1.628077
75%        5.717506
max      146.894973
Name: 19&over, dtype: float64

Summary Statistics for 19&over - Girls:
count    143.000000
mean       5.252990
std       32.134173
min      -62.699387
25%       -1.342725
50%        1.596916
75%        5.746109
max      155.458515
Name: 19&over, dtype: float64

T-test results for 19&over:
T-statistic: -0.9512455803908627
P-value: 0.3424329532873308

The difference is not statistically significant.

Summary Statistics for 17-18 - Boys:
count    143.000000
mean       0.593595
std        8.395509
min      -29.849057
25%       -2.646057
50%       -0.207727
75%        2.635111
max       52.298851
Name: 17-18, dtype: float64

Summary Statistics for 17-18 - Girls:
count     143.000000
mean       70.349035
std       282.418056
min       -94.942529
25%        -4.942631
50%         2.265372
75%         8.493645
max      1904.545455
Name: 17-18, dtype: float64

T-test results for 17-18:
T-statistic: -2.952309135185616
P-value: 0.0036908172111356522

The difference is statistically significant.

Summary Statistics for 15-16 - Boys:
count    143.000000
mean       1.045745
std        6.243847
min      -21.761281
25%       -1.796867
50%        0.977995
75%        3.379656
max       36.354615
Name: 15-16, dtype: float64

Summary Statistics for 15-16 - Girls:
count    143.000000
mean       6.390607
std       27.327220
min      -50.115473
25%       -2.822217
50%        3.929916
75%        9.047655
max      125.462963
Name: 15-16, dtype: float64

T-test results for 15-16:
T-statistic: -2.280125930979017
P-value: 0.023947992795947972

The difference is statistically significant.

Summary Statistics for 13-14 - Boys:
count    143.000000
mean       0.649856
std        7.174630
min      -34.873141
25%       -1.498996
50%        0.571874
75%        2.476904
max       63.218700
Name: 13-14, dtype: float64

Summary Statistics for 13-14 - Girls:
count    143.000000
mean       4.880664
std       18.163781
min      -34.168157
25%       -2.133861
50%        2.118644
75%        7.397198
max       85.869565
Name: 13-14, dtype: float64

T-test results for 13-14:
T-statistic: -2.5906097359693576
P-value: 0.010343043416480518

The difference is statistically significant.

Summary Statistics for 11-12 - Boys:
count    143.000000
mean       0.499813
std       10.532733
min      -45.460076
25%       -2.329878
50%       -0.198659
75%        2.765759
max      101.505856
Name: 11-12, dtype: float64

Summary Statistics for 11-12 - Girls:
count    143.000000
mean       4.102480
std       11.635099
min      -30.040595
25%       -1.396142
50%        2.486679
75%        8.443674
max       46.851385
Name: 11-12, dtype: float64

T-test results for 11-12:
T-statistic: -2.745033120880804
P-value: 0.006440082127097802

The difference is statistically significant.

Summary Statistics for 9-10 - Boys:
count    143.000000
mean       1.256297
std       14.968338
min      -56.518023
25%       -2.185239
50%        0.573066
75%        3.339835
max      145.937090
Name: 9-10, dtype: float64

Summary Statistics for 9-10 - Girls:
count    143.000000
mean       4.951924
std       12.206388
min      -30.386052
25%       -0.940258
50%        2.824268
75%        8.492017
max       56.171735
Name: 9-10, dtype: float64

T-test results for 9-10:
T-statistic: -2.288096703813683
P-value: 0.022896576713507373

The difference is statistically significant.

Summary Statistics for 7-8 - Boys:
count    143.000000
mean       2.417437
std       20.156915
min      -64.898670
25%       -3.682709
50%        1.343101
75%        5.853801
max      180.108254
Name: 7-8, dtype: float64

Summary Statistics for 7-8 - Girls:
count    143.000000
mean       6.164154
std       11.383932
min      -31.350682
25%       -0.081384
50%        5.409467
75%       11.483813
max       56.403270
Name: 7-8, dtype: float64

T-test results for 7-8:
T-statistic: -1.9354371588970642
P-value: 0.05419420412246262

The difference is not statistically significant.

Summary Statistics for 6&U - Boys:
count    143.000000
mean       6.777624
std       62.291285
min      -86.649660
25%       -3.023863
50%        0.576067
75%        5.984290
max      711.719745
Name: 6&U, dtype: float64

Summary Statistics for 6&U - Girls:
count    143.000000
mean       6.691576
std       19.412449
min      -42.794118
25%       -3.241717
50%        4.582651
75%       13.305609
max       70.951157
Name: 6&U, dtype: float64

T-test results for 6&U:
T-statistic: 0.015770823907187034
P-value: 0.9874357918863546

The difference is not statistically significant.


# Combine the boys and girls growth DataFrames
combined_growth = pd.merge(boys_district_growth, girls_district_growth, on=['Season_Start', 'District'], suffixes=('_boys', '_girls'))

# Display the combined growth DataFrame
print(combined_growth)

     Season_Start  District  All_Ages_boys  19&over_boys  17-18_boys  \
0          2012.0         1       0.973258      7.419867   -2.725780   
1          2013.0         1       1.097337     -1.618095   -4.456448   
2          2014.0         1      -0.868341      0.123795   -1.201413   
3          2015.0         1      -0.396544      1.969443    0.572246   
4          2016.0         1       2.929467      0.363762   -0.568990   
..            ...       ...            ...           ...         ...   
138        2018.0        13       0.478469      0.860727    1.320340   
139        2019.0        13      -1.546485     -2.231212   -0.868756   
140        2020.0        13     -19.717419    -36.692073    9.565728   
141        2021.0        13      19.829016     44.585829   -4.542046   
142        2022.0        13       1.191404      2.192970   -0.665844   

     15-16_boys  13-14_boys  11-12_boys  9-10_boys   7-8_boys  ...  \
0     -2.188070    2.981239   -3.442584  -2.847571  -3.475046  ...   
1     -1.970706    2.994759    0.830281   5.775862   5.898123  ...   
2      0.977995    1.526533   -0.726568  -3.205651  -3.182640  ...   
3     -0.430455   -0.835322    1.244206  -4.771260  -4.482630  ...   
4      2.864091   -0.890493   -4.120482   1.532567  13.179507  ...   
..          ...         ...         ...        ...        ...  ...   
138    0.702840   -1.496491    0.254185   1.358171   0.035342  ...   
139    0.747415   -1.027535   -0.815085  -1.131288  -1.897976  ...   
140    6.046115    1.161895   -4.605014 -10.803555 -20.825497  ...   
141   -4.994516   -1.858125    2.042477   9.722885  22.073131  ...   
142    1.955049    0.067399    0.299558  -0.491875   4.578952  ...   

     19&over_girls  17-18_girls  15-16_girls  13-14_girls  11-12_girls  \
0        -2.386364    -9.132420    -3.934426     0.000000     7.466667   
1         4.190920     5.025126    -2.389078    20.710059    -4.714640   
2         0.223464     2.392344    27.272727    11.274510    -2.864583   
3        -2.564103    -8.411215    15.659341   -10.792952    13.136729   
4        -0.572082    13.265306   -10.688836    -2.469136     5.924171   
..             ...          ...          ...          ...          ...   
138       4.060527     2.418896     4.161692     1.619528     5.432024   
139      -0.460299     4.167824     3.929916     1.547523     3.264638   
140     -47.850762   -86.743132   -38.270049   -24.007733   -15.975007   
141      81.660621   748.490946    73.124043    47.306196    27.458380   
142       8.320391     2.916765     4.643963     3.078330     3.857541   

     9-10_girls  7-8_girls  6&U_girls  Season_1_Compared_girls  \
0     -9.034268  -2.272727 -10.236220                   2011.0   
1      6.849315  15.348837  66.666667                   2012.0   
2     13.141026   1.209677 -21.052632                   2013.0   
3     19.546742  11.952191  18.666667                   2014.0   
4     -4.502370  29.893238  64.606742                   2015.0   
..          ...        ...        ...                      ...   
138    7.880384   4.669587   3.082624                   2017.0   
139    2.824268  -0.026714   1.617779                   2018.0   
140  -12.851814  -4.551528 -22.272252                   2019.0   
141   21.410506  13.503173  34.188476                   2020.0   
142    2.684080   5.179643  -2.821462                   2021.0   

     Season_2_Compared_girls  
0                     2012.0  
1                     2013.0  
2                     2014.0  
3                     2015.0  
4                     2016.0  
..                       ...  
138                   2018.0  
139                   2019.0  
140                   2020.0  
141                   2021.0  
142                   2022.0  

[143 rows x 24 columns]


# Define the age groups
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

# Create an empty list to store dictionaries
lower_growth_info_list = []

# Iterate through age groups
for age_group in age_groups:
    # Extract growth values for boys and girls
    boys_growth = boys_district_growth[age_group]
    girls_growth = girls_district_growth[age_group]

    # Find where girls show lower growth than boys
    lower_growth_mask = girls_growth < boys_growth

    # Extract relevant information and append to the list
    for i in range(len(boys_growth[lower_growth_mask])):
        lower_growth_info_list.append({
            'Season_Start': boys_district_growth['Season_Start'][lower_growth_mask].iloc[i],
            'District': boys_district_growth['District'][lower_growth_mask].iloc[i],
            'Age_Group': age_group,
            'Lower_Growth_Percentage': (boys_growth - girls_growth)[lower_growth_mask].iloc[i]
        })

# Create DataFrame from the list
lower_growth_info = pd.DataFrame(lower_growth_info_list)

# Display the results
lower_growth_info.head()


# Assuming lower_growth_info is the DataFrame containing the information
most_common_district = lower_growth_info['District'].value_counts().idxmax()
most_common_age_group = lower_growth_info['Age_Group'].value_counts().idxmax()

print("Most Common District:", most_common_district)
print("Most Common Age Group:", most_common_age_group)

Most Common District: 9
Most Common Age Group: 19&over


# Assuming lower_growth_info is the DataFrame containing the information
most_common_combination = lower_growth_info.groupby(['District', 'Age_Group']).size().idxmax()

print("Most Common Combination (District, Age Group):", most_common_combination)

Most Common Combination (District, Age Group): (7, '17-18')


# Assuming lower_growth_info is the DataFrame containing the information
combination_counts = lower_growth_info.groupby(['District', 'Age_Group']).size().reset_index(name='Combination_Count')
sorted_combination_counts = combination_counts.sort_values(by='Combination_Count', ascending=False)

print("Sorted Combination Counts:")
print(sorted_combination_counts)

Sorted Combination Counts:
     District Age_Group  Combination_Count
56          7     17-18                  8
72          9     13-14                  8
57          7   19&over                  8
39          5   19&over                  7
73          9     15-16                  7
..        ...       ...                ...
106        12  All_Ages                  1
49          6       6&U                  1
61          7  All_Ages                  1
6           1       7-8                  1
115        13  All_Ages                  1

[116 rows x 3 columns]


# Plotting growth for each age group
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

for age_group in age_groups:
    plt.figure(figsize=(10, 6))
    plt.plot(combined_growth['Season_Start'], combined_growth[f'{age_group}_boys'], label=f'Boys - {age_group}')
    plt.plot(combined_growth['Season_Start'], combined_growth[f'{age_group}_girls'], label=f'Girls - {age_group}')
    plt.xlabel('Season Start Year')
    plt.ylabel('Growth (%)')
    plt.title(f'Comparison of Growth for {age_group} between Boys and Girls Districts')
    plt.legend()
    plt.grid(True)
    plt.show()


# Assuming boys_district_growth and girls_district_growth DataFrames
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

for age_group in age_groups:
    boys_growth = boys_district_growth[age_group]
    girls_growth = girls_district_growth[age_group]

    plt.figure(figsize=(10, 6))
    plt.bar(boys_growth.index - 0.2, boys_growth, width=0.4, label='Boys', align='center')
    plt.bar(girls_growth.index + 0.2, girls_growth, width=0.4, label='Girls', align='center')

    plt.title(f'Growth Comparison for {age_group}')
    plt.xlabel('District')
    plt.ylabel('Growth')
    plt.legend()
    plt.show()


# Columns to include in the plot
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

# Bar width
bar_width = 0.35

# Positions for each age group on X-axis
index = np.arange(len(age_groups))

# Make the graph wider
fig, ax = plt.subplots(figsize=(12, 6))

# Bar graph for boys
ax.bar(index, growth_boys_district_13[age_groups].iloc[0], bar_width, label='Boys')

# Bar graph for girls
ax.bar(index + bar_width, growth_girls_district_13[age_groups].iloc[0], bar_width, label='Girls')

# Labels and title
ax.set_xlabel('Age Groups')
ax.set_ylabel('Growth (%)')
ax.set_title('Growth Comparison between Boys and Girls in District 13')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(age_groups)

# Place the legend above the bars
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=2)

plt.show()


# Selecting relevant columns from all_state and girls_state
all_state_subset = all_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
girls_state_subset = girls_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]

# Subtracting girls_district values from all_district values
boys_state = all_state_subset.copy()
boys_state[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']] -= girls_state_subset[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]

# Displaying the resulting DataFrame (boys_df)
boys_state.head()


def calculate_season_to_season_growth(df):
    """
    Calculates season-to-season growth for each state in the given DataFrame.

    Parameters:
    - df: DataFrame with columns 'Season_Start', 'State', and numeric columns for age groups.

    Returns:
    - growth_df: DataFrame with season-to-season growth for each state including the seasons being compared.
    """
    # Sort DataFrame by 'Season_Start' for chronological order
    df = df.sort_values(by=['State', 'Season_Start'])

    # Group by 'State' and calculate growth
    growth_df = pd.DataFrame()
    for state, group_df in df.groupby('State'):
        # Calculate growth for numeric columns
        growth = group_df.set_index('Season_Start').pct_change().dropna() * 100
        growth['State'] = state
        growth.reset_index(inplace=True)

        # Add columns for the seasons being compared
        growth['Season_1_Compared'] = growth['Season_Start'].shift(1)
        growth['Season_2_Compared'] = growth['Season_Start']

        # Append the calculated growth DataFrame to the result DataFrame
        growth_df = pd.concat([growth_df, growth], ignore_index=True)

    return growth_df

# Example usage:
# Assuming df is your DataFrame with columns 'Season_Start', 'State', and numeric columns for age groups.
# growth_result = calculate_season_to_season_growth(df)


boys_growth_state = calculate_season_to_season_growth(boys_state)
girls_growth_state = calculate_season_to_season_growth(girls_state)


boys_growth_state.head()


boys_growth_state['Season_1_Compared'].fillna(2011, inplace=True)
girls_growth_state['Season_1_Compared'].fillna(2011, inplace=True)


girls_growth_state.head()


def calculate_2011_2022_growth(dataframe):
    # Filter data for Season_Start 2011 and 2022
    season_2011 = dataframe[dataframe['Season_Start'] == 2011]
    season_2022 = dataframe[dataframe['Season_Start'] == 2022]

    # Merge dataframes on State to get corresponding values
    merged_data = pd.merge(season_2011, season_2022, on='State', suffixes=('_2011', '_2022'))

    # Initialize a new DataFrame for growth
    growth_data = pd.DataFrame()
    growth_data['State'] = merged_data['State']

    # Calculate growth for each age group
    for age_group in ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']:
        numerator = merged_data[f'{age_group}_2022'] - merged_data[f'{age_group}_2011']
        denominator = merged_data[f'{age_group}_2011']

        # Replace zero values in the denominator with 1
        denominator = np.where(denominator == 0, 1, denominator)

        # Calculate growth percentage and store in the new dataframe
        growth_data[age_group] = (numerator / denominator) * 100

    return growth_data

# Example usage:
# Assuming your dataframe is named 'df'
# growth_result = calculate_growth(df)
# print(growth_result)


boys_growth_result = calculate_2011_2022_growth(boys_state)
boys_growth_result.head()


girls_growth_result = calculate_2011_2022_growth(girls_state)
girls_growth_result.head()


print(girls_growth_state.columns)

Index(['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18',
       '15-16', '13-14', '11-12', '9-10', '7-8', '6&U', 'Season_1_Compared',
       'Season_2_Compared'],
      dtype='object')


print(boys_growth_state.columns)

Index(['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18',
       '15-16', '13-14', '11-12', '9-10', '7-8', '6&U', 'Season_1_Compared',
       'Season_2_Compared'],
      dtype='object')


import pandas as pd
from scipy.stats import ttest_ind

# Assuming boys_growth_state and girls_growth_state are your DataFrames

age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

for age_group in age_groups:
    # Check if the column exists in boys_growth_state DataFrame
    if age_group in boys_growth_state.columns:
        boys_growth_state_age_group = boys_growth_state[age_group].dropna()
        girls_growth_state_age_group = girls_growth_state[age_group].dropna()

        # Summary Statistics for Boys
        boys_summary = boys_growth_state_age_group.describe()

        # Summary Statistics for Girls
        girls_summary = girls_growth_state_age_group.describe()

        # T-test
        t_statistic, p_value = ttest_ind(boys_growth_state_age_group, girls_growth_state_age_group, equal_var=False)

        print(f"\nSummary Statistics for {age_group} - Boys:")
        print(boys_summary)

        print(f"\nSummary Statistics for {age_group} - Girls:")
        print(girls_summary)

        print(f"\nT-test results for {age_group}:")
        print(f"T-statistic: {t_statistic}")
        print(f"P-value: {p_value}")

        # Check for statistical significance
        if p_value < 0.05:
            print("The difference is statistically significant.")
        else:
            print("The difference is not statistically significant.")
    else:
        print(f"Column '{age_group}' not found in boys_growth_state DataFrame.")

Summary Statistics for All_Ages - Boys:
count    577.000000
mean       3.665880
std       43.366235
min      -95.625000
25%       -2.258852
50%        1.077844
75%        5.009185
max      979.166667
Name: All_Ages, dtype: float64

Summary Statistics for All_Ages - Girls:
count    556.000000
mean       5.941174
std       17.175375
min      -60.169492
25%       -0.737704
50%        3.907092
75%        9.583448
max      117.021277
Name: All_Ages, dtype: float64

T-test results for All_Ages:
T-statistic: -1.1687573290849314
P-value: 0.24286887218525535
The difference is not statistically significant.

Summary Statistics for 19&over - Boys:
count     577.000000
mean        6.447437
std        56.443760
min       -95.319149
25%        -4.685408
50%         1.644962
75%         7.785825
max      1062.500000
Name: 19&over, dtype: float64

Summary Statistics for 19&over - Girls:
count    556.000000
mean       8.623032
std       40.068148
min      -79.762913
25%       -4.599567
50%        2.186837
75%       11.764706
max      344.769874
Name: 19&over, dtype: float64

T-test results for 19&over:
T-statistic: -0.7502500904053564
P-value: 0.453273689606679
The difference is not statistically significant.

Summary Statistics for 17-18 - Boys:
count    577.000000
mean            inf
std             NaN
min     -100.000000
25%       -6.146572
50%        0.000000
75%        7.430341
max             inf
Name: 17-18, dtype: float64

Summary Statistics for 17-18 - Girls:
count    556.000000
mean            inf
std             NaN
min     -100.000000
25%      -11.111111
50%        1.712529
75%       24.431818
max             inf
Name: 17-18, dtype: float64

T-test results for 17-18:
T-statistic: nan
P-value: nan
The difference is not statistically significant.

Summary Statistics for 15-16 - Boys:
count    577.000000
mean            inf
std             NaN
min     -100.000000
25%       -3.636364
50%        1.358025
75%        6.382979
max             inf
Name: 15-16, dtype: float64

Summary Statistics for 15-16 - Girls:
count    556.000000
mean            inf
std             NaN
min     -100.000000
25%       -8.163410
50%        4.650141
75%       19.211666
max             inf
Name: 15-16, dtype: float64

T-test results for 15-16:
T-statistic: nan
P-value: nan
The difference is not statistically significant.

Summary Statistics for 13-14 - Boys:
count    577.000000
mean       2.763956
std       18.933912
min      -90.000000
25%       -2.762431
50%        0.673077
75%        6.008584
max      250.000000
Name: 13-14, dtype: float64

Summary Statistics for 13-14 - Girls:
count    556.000000
mean            inf
std             NaN
min     -100.000000
25%       -4.545455
50%        3.554203
75%       15.023585
max             inf
Name: 13-14, dtype: float64

T-test results for 13-14:
T-statistic: nan
P-value: nan
The difference is not statistically significant.

Summary Statistics for 11-12 - Boys:
count     577.000000
mean        4.345506
std        50.389936
min      -100.000000
25%        -3.303571
50%         0.500626
75%         6.071429
max      1100.000000
Name: 11-12, dtype: float64

Summary Statistics for 11-12 - Girls:
count    556.000000
mean       9.430226
std       29.643914
min      -75.000000
25%       -3.508786
50%        5.250033
75%       16.666667
max      300.000000
Name: 11-12, dtype: float64

T-test results for 11-12:
T-statistic: -2.0791041667980226
P-value: 0.03787888769548562
The difference is statistically significant.

Summary Statistics for 9-10 - Boys:
count     577.000000
mean        6.049937
std        81.721214
min       -94.444444
25%        -4.070289
50%         1.025641
75%         6.959707
max      1900.000000
Name: 9-10, dtype: float64

Summary Statistics for 9-10 - Girls:
count    556.000000
mean       9.963006
std       33.568354
min     -100.000000
25%       -5.882353
50%        3.957140
75%       17.722474
max      255.555556
Name: 9-10, dtype: float64

T-test results for 9-10:
T-statistic: -1.0610426791638377
P-value: 0.2890028249113801
The difference is not statistically significant.

Summary Statistics for 7-8 - Boys:
count    577.000000
mean            inf
std             NaN
min     -100.000000
25%       -6.140351
50%        1.549681
75%        9.308511
max             inf
Name: 7-8, dtype: float64

Summary Statistics for 7-8 - Girls:
count    556.000000
mean      13.927815
std       44.660714
min      -75.000000
25%       -6.079265
50%        5.970822
75%       22.435897
max      300.000000
Name: 7-8, dtype: float64

T-test results for 7-8:
T-statistic: nan
P-value: nan
The difference is not statistically significant.

Summary Statistics for 6&U - Boys:
count    577.000000
mean            inf
std             NaN
min      -93.750000
25%       -7.899136
50%        0.212314
75%       11.111111
max             inf
Name: 6&U, dtype: float64

Summary Statistics for 6&U - Girls:
count    556.000000
mean            inf
std             NaN
min     -100.000000
25%       -9.104767
50%        4.517671
75%       23.543285
max             inf
Name: 6&U, dtype: float64

T-test results for 6&U:
T-statistic: nan
P-value: nan
The difference is not statistically significant.

E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract
  a_zero_mean = a - mean
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:6999: RuntimeWarning: invalid value encountered in scalar subtract
  d = mean1 - mean2
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:7474: RuntimeWarning: invalid value encountered in scalar subtract
  estimate = m1-m2
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract
  a_zero_mean = a - mean
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:6999: RuntimeWarning: invalid value encountered in scalar subtract
  d = mean1 - mean2
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:7474: RuntimeWarning: invalid value encountered in scalar subtract
  estimate = m1-m2
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract
  a_zero_mean = a - mean
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract
  a_zero_mean = a - mean
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract
  a_zero_mean = a - mean
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:6999: RuntimeWarning: invalid value encountered in scalar subtract
  d = mean1 - mean2
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:7474: RuntimeWarning: invalid value encountered in scalar subtract
  estimate = m1-m2


# Combine the boys and girls growth DataFrames
combined_growth_state = pd.merge(boys_growth_state, girls_growth_state, on=['Season_Start', 'District', 'State'], suffixes=('_boys', '_girls'))

# Display the combined growth DataFrame
print(combined_growth_state)

     Season_Start  District  State  All_Ages_boys  19&over_boys  17-18_boys  \
0          2012.0       0.0    1.1     -15.400203     -6.168831  -26.984127   
1          2013.0       0.0    1.1       1.077844     -5.536332    5.434783   
2          2014.0       0.0    1.1       4.502370      4.029304   10.309278   
3          2015.0       0.0    1.1      -1.587302      1.760563  -25.233645   
4          2016.0       0.0    1.1       2.073733     -5.882353   11.250000   
..            ...       ...    ...            ...           ...         ...   
551        2018.0       0.0   13.0       0.478469      0.860727    1.320340   
552        2019.0       0.0   13.0      -1.546485     -2.231212   -0.868756   
553        2020.0       0.0   13.0     -19.717419    -37.004616   -3.818466   
554        2021.0       0.0   13.0      19.829016     45.303172    8.741458   
555        2022.0       0.0   13.0       1.191404      2.192970   -0.665844   

     15-16_boys  13-14_boys  11-12_boys  9-10_boys  ...  19&over_girls  \
0    -23.611111  -11.926606  -18.018018 -14.432990  ...     -18.032787   
1     -1.818182   16.666667   10.989011  -7.228916  ...     -18.000000   
2      0.925926    0.000000    4.950495  -6.493506  ...       9.756098   
3      1.834862    0.000000    0.000000   9.722222  ...      -8.888889   
4     -6.306306    4.464286  -17.924528  -1.265823  ...      -2.439024   
..          ...         ...         ...        ...  ...            ...   
551    0.702840   -1.496491    0.254185   1.358171  ...       4.060527   
552    0.747415   -1.027535   -0.815085  -1.131288  ...      -0.460299   
553   -1.098589   -3.558683   -7.009771 -11.610813  ...     -45.239096   
554    1.868744    2.945691    4.681328  10.724984  ...      72.996833   
555    1.955049    0.067399    0.299558  -0.491875  ...       8.320391   

     17-18_girls  15-16_girls  13-14_girls  11-12_girls  9-10_girls  \
0     -66.666667     0.000000   500.000000    25.000000  -33.333333   
1     100.000000   -83.333333     0.000000   -40.000000  150.000000   
2       0.000000   300.000000    16.666667    33.333333   80.000000   
3     -83.333333   125.000000   -14.285714    75.000000  -33.333333   
4     700.000000   -22.222222    83.333333    42.857143   50.000000   
..           ...          ...          ...          ...         ...   
551     2.418896     4.161692     1.619528     5.432024    7.880384   
552     4.167824     3.929916     1.547523     3.264638    2.824268   
553     4.507869     5.278084     2.467872    -4.131307   -9.155646   
554     7.631445     1.511523     9.245283    11.712062   16.470698   
555     2.916765     4.643963     3.078330     3.857541    2.684080   

      7-8_girls   6&U_girls  Season_1_Compared_girls  Season_2_Compared_girls  
0    100.000000  100.000000                   2011.0                   2012.0  
1      0.000000  150.000000                   2012.0                   2013.0  
2      0.000000  -80.000000                   2013.0                   2014.0  
3     75.000000  300.000000                   2014.0                   2015.0  
4     14.285714  250.000000                   2015.0                   2016.0  
..          ...         ...                      ...                      ...  
551    4.669587    3.082624                   2017.0                   2018.0  
552   -0.026714    1.617779                   2018.0                   2019.0  
553  -13.895074  -21.974753                   2019.0                   2020.0  
554   25.819799   33.676834                   2020.0                   2021.0  
555    5.179643   -2.821462                   2021.0                   2022.0  

[556 rows x 25 columns]


import pandas as pd

# Assuming girls_growth_state and boys_growth_state are your DataFrames

# Align indices of both DataFrames
girls_growth_state, boys_growth_state = girls_growth_state.align(boys_growth_state, join='inner')

# Find where girls show lower growth than boys for all columns
lower_growth_mask = girls_growth_state < boys_growth_state

# Create an empty list to store the results
result_list = []

# Iterate through all columns and extract relevant information
for age_group in girls_growth_state.columns:
    # Exclude 'Season_Start' and 'District' columns from comparison
    if age_group in ['Season_Start', 'District', 'Season_1_Compared', 'Season_2_Compared']:
        continue

    # Extract relevant information and append to the result list
    for index, row in girls_growth_state[lower_growth_mask[age_group]].iterrows():
        season_start = index
        state = row['State']  # Assuming 'State' is a column in your DataFrame
        girl_growth = row[age_group]
        boy_growth = boys_growth_state.loc[index, age_group]

        result_list.append({
            'Season_Start': season_start,
            'State': state,
            'Age_Group': age_group,
            'Girl_Growth': girl_growth,
            'Boy_Growth': boy_growth
        })

# Create the result DataFrame
lower_growth_info_state = pd.DataFrame(result_list)

# Display the result DataFrame
print(lower_growth_info_state)

      Season_Start  State Age_Group  Girl_Growth  Boy_Growth
0                1    1.1  All_Ages    -8.974359    1.077844
1                6    1.1  All_Ages    -6.956522   -3.257329
2                8    1.1  All_Ages   -25.892857  -15.320665
3               11    1.2  All_Ages    -5.186170    0.297827
4               15    1.2  All_Ages    -2.110977    3.077528
...            ...    ...       ...          ...         ...
2064           547   13.0       6&U     3.610808    6.161137
2065           548   13.0       6&U    13.320080   50.000000
2066           549   13.0       6&U    13.498452   42.857143
2067           553   13.0       6&U   -21.974753   12.406015
2068           555   13.0       6&U    -2.821462   11.851852

[2069 rows x 5 columns]


# Assuming lower_growth_info is the DataFrame containing the information

most_common_state = lower_growth_info_state['State'].value_counts().idxmax()
most_common_age_group_state = lower_growth_info_state['Age_Group'].value_counts().idxmax()

print("Most Common State:", most_common_state)
print("Most Common Age Group:", most_common_age_group_state)

Most Common State: 10.5
Most Common Age Group: 19&over


# Assuming lower_growth_info is the DataFrame containing the information

most_common_combination_state = lower_growth_info_state.groupby(['State', 'Age_Group']).size().idxmax()

print("Most Common Combination (State, Age Group):", most_common_combination_state)

Most Common Combination (State, Age Group): (5.2, '15-16')


# Assuming lower_growth_info is the DataFrame containing the information
combination_counts_state = lower_growth_info_state.groupby(['State', 'Age_Group']).size().reset_index(name='Combination_Count')
sorted_combination_counts_state = combination_counts_state.sort_values(by='Combination_Count', ascending=False)

print("Sorted Combination Counts:")
print(sorted_combination_counts_state)

Sorted Combination Counts:
     State Age_Group  Combination_Count
181    7.4     17-18                  8
108    5.2     15-16                  8
225    9.3     15-16                  8
344   11.7   19&over                  8
129    5.4       6&U                  8
..     ...       ...                ...
0      1.1     11-12                  2
105    5.1  All_Ages                  1
147    6.0       6&U                  1
149    6.0      9-10                  1
34     2.1      9-10                  1

[466 rows x 3 columns]


# Plotting growth for each age group
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

for age_group in age_groups:
    plt.figure(figsize=(10, 6))
    plt.plot(combined_growth_state['Season_Start'], combined_growth_state[f'{age_group}_boys'], label=f'Boys - {age_group}')
    plt.plot(combined_growth_state['Season_Start'], combined_growth_state[f'{age_group}_girls'], label=f'Girls - {age_group}')
    plt.xlabel('Season Start Year')
    plt.ylabel('Growth (%)')
    plt.title(f'Comparison of Growth for {age_group} between Boys and Girls States')
    plt.legend()
    plt.grid(True)
    plt.show()


import matplotlib.pyplot as plt

# Assuming boys_growth_state and girls_growth_state are your DataFrames

# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')

# Plot growth for each age group
for age_group in boys_growth_state.columns:
    # Exclude 'Season_Start', 'District', 'State' columns from comparison
    if age_group in ('Season_Start', 'District', 'State', 'Season_1_Compared', 'Season_2_Compared'):
        continue

    # Plot boys' and girls' growth for the age group
    plt.figure(figsize=(10, 6))
    plt.plot(boys_growth_state['Season_Start'], boys_growth_state[age_group], label='Boys')
    plt.plot(girls_growth_state['Season_Start'], girls_growth_state[age_group], label='Girls')
    plt.title(f'Growth Comparison - {age_group}')
    plt.xlabel('Season Start')
    plt.ylabel('Growth')
    plt.legend()
    plt.show()


import matplotlib.pyplot as plt

# Assuming boys_growth_state and girls_growth_state are your DataFrames

# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')

# Specify age groups
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

# Plot growth for each age group
for age_group in age_groups:
    # Filter DataFrame for the specified age group
    boys_growth_state_age_group = boys_growth_state[[age_group]].dropna()
    girls_growth_state_age_group = girls_growth_state[[age_group]].dropna()

    # Plot boys' and girls' growth for the age group
    plt.figure(figsize=(10, 6))
    plt.plot(boys_growth_state_age_group, label='Boys')
    plt.plot(girls_growth_state_age_group, label='Girls')
    plt.title(f'Growth Comparison - {age_group}')
    plt.xlabel('Season Start')
    plt.ylabel('Growth')
    plt.legend()
    plt.show()


# Columns to include in the plot
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']

# Bar width
bar_width = 0.35

# Positions for each age group on X-axis
index = np.arange(len(age_groups))

# Make the graph wider
fig, ax = plt.subplots(figsize=(12, 6))

# Bar graph for boys
ax.bar(index, boys_growth_result[age_groups].iloc[0], bar_width, label='Boys')

# Bar graph for girls
ax.bar(index + bar_width, girls_growth_result[age_groups].iloc[0], bar_width, label='Girls')

# Labels and title
ax.set_xlabel('Age Groups')
ax.set_ylabel('Growth (%)')
ax.set_title('Growth Comparison between Boys and Girls in States, 2011-2022')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(age_groups)

# Place the legend above the bars
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=2)

plt.show()


from sklearn.linear_model import LinearRegression
import numpy as np

# Assuming boys_growth_state and girls_growth_state are your DataFrames

# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')

# Iterate over each age group column
for age_group in boys_growth_state.columns:
    # Exclude 'Season_Start', 'District', 'State' columns from comparison
    if age_group in ('Season_Start', 'District', 'State'):
        continue

    # Extract x and y values for regression
    x = boys_growth_state['Season_Start'].values.reshape(-1, 1)
    y_boys = boys_growth_state[age_group].values.reshape(-1, 1)
    y_girls = girls_growth_state[age_group].values.reshape(-1, 1)

    # Remove NaN, infinity, and very large values
    mask_boys = np.isfinite(y_boys) & (y_boys < 1e10)
    mask_girls = np.isfinite(y_girls) & (y_girls < 1e10)

    x = x[mask_boys & mask_girls].reshape(-1, 1)
    y_boys = y_boys[mask_boys & mask_girls].reshape(-1, 1)
    y_girls = y_girls[mask_boys & mask_girls].reshape(-1, 1)

    # Fit linear regression models
    model_boys = LinearRegression().fit(x, y_boys)
    model_girls = LinearRegression().fit(x, y_girls)

    # Predict y values using the models
    y_pred_boys = model_boys.predict(x)
    y_pred_girls = model_girls.predict(x)

    # Print boys' and girls' growth for the age group
    print(f'Age Group: {age_group}')
    print('Boys Growth:')
    print('Actual:', y_boys.flatten())
    print('Predicted:', y_pred_boys.flatten())
    print('\nGirls Growth:')
    print('Actual:', y_girls.flatten())
    print('Predicted:', y_pred_girls.flatten())
    print('\n')

Age Group: All_Ages
Boys Growth:
Actual: [-1.54002026e+01  1.07784431e+00  4.50236967e+00 -1.58730159e+00
  2.07373272e+00  3.95033860e+00 -3.25732899e+00 -5.49943883e+00
 -1.53206651e+01  1.69705470e+01 -2.99760192e+00  2.97826500e-01
  1.85746778e+00 -3.24401439e+00 -2.09628822e+00  3.07752750e+00
 -1.10532334e+00 -1.86279548e-01 -5.51515542e+00 -1.40512192e+01
  1.31230684e+01 -2.26970228e+00  2.58646617e+00  3.92846673e-01
  1.10384301e+00  1.19577147e+00  2.84279027e+00 -1.49311723e+00
  3.91052009e+00 -5.43354482e+00 -2.16182121e+01  1.81139805e+01
  2.97305667e+00  4.67254566e+00  8.68037532e+00  1.81490870e+00
  3.03106845e+00 -2.66171681e-01  2.55996067e+00 -6.31377114e+00
 -2.81412177e+00 -2.18712395e+01  1.15325375e+01 -3.70706025e+00
 -5.57980050e+00  2.17893694e+00  7.72213247e+00 -1.13977205e+00
  2.03276699e+00  3.86559619e-01 -6.63507109e+00 -6.09137056e+00
 -2.77027027e+00  1.25781793e+01 -2.50000000e+00 -2.17025749e+01
  3.89261745e+00 -6.45994832e-01 -5.91677503e+00  6.21976503e+00
  1.69160703e+00  4.41458733e+00  1.10294118e+00 -9.69696970e+00
  8.25503356e+00  4.15375077e+00  2.31343284e+00 -2.69876003e+00
 -2.21889055e+00  1.37994480e+00  6.53357532e+00 -2.99545713e+00
  2.76598858e+00  2.72002279e+01 -1.08262427e+01  1.86315129e+01
  6.79436977e+00 -5.51989730e+00  5.70652174e+00  6.55526992e+00
  5.48854041e+00  2.22984563e+00 -2.12527964e+00 -2.00000000e+00
  1.34110787e+00 -3.73993096e+00  1.32098027e+01 -2.11193242e-01
 -1.67191997e+00  2.18815331e+00  1.09110747e+00  2.92768484e+00
  3.15244462e+00  1.42321621e+00  9.64730940e-01 -2.53769312e+00
 -7.72217978e+00  1.11072784e+01  9.99689537e-01  8.05931657e-02
  2.69501262e+00  2.67395055e+00 -1.83549299e+00  2.20435685e-01
 -2.39358261e+00 -9.49098621e-01 -1.20978534e+00 -1.28420482e+01
  1.03947777e+01 -2.84958045e+00 -2.15834782e+00 -2.76855396e+00
 -3.56738885e-01 -1.68092910e+00 -8.21528487e-02 -2.89549121e+00
 -6.86530276e-01 -4.47946910e+00 -3.46625175e+01  1.85600886e+01
 -1.39200299e+00  6.24630542e+00  1.89169139e+00  3.54932654e+00
  2.93548954e+00  4.78142077e-01 -3.39904827e-02  2.80516831e+00
 -5.25880602e+00 -8.48315587e+00  1.26072859e+01 -1.16869919e+00
 -2.26214238e+00  7.48808713e-01  1.14864865e+00 -1.20240481e+00
 -1.41987830e+00  1.65980796e+01  3.64705882e+00 -2.32690125e+00
 -1.67344567e+01  1.89811584e+01  4.45747801e+00 -1.86414138e-01
 -1.86762289e-01  2.41748372e+00  1.35925168e+00  3.12184571e+00
  1.31440956e+00  2.80863985e+00 -9.46435763e-01 -1.00359152e+01
  1.56447725e+01  3.90803100e+00  3.62896241e+00 -2.11450063e+00
  8.28210383e-01  7.10475061e+00  5.67678684e+00  2.47643274e+00
  6.05972111e-01 -4.92743106e+00 -1.57163575e+01  8.25937330e+00
  1.17115610e+00  1.09561753e+00 -2.95566502e-01  1.67984190e+00
 -3.10981535e+00  3.30992979e+00  4.17475728e+00  9.31966449e-02
 -1.65735568e+01 -2.48883929e+01  2.77860327e+01 -4.30232558e+00
 -1.60221517e+00  8.87509921e-01  1.65927621e+00  2.60541250e+00
 -3.51975865e-01  3.48631849e-01 -1.16568764e-01 -1.01601831e+00
 -5.10911781e+00  5.46460069e+00  6.58365867e-01  7.73338243e-01
 -9.68390280e-01  1.39298893e+00 -2.11081794e+00 -1.48712706e+00
 -1.42466270e+00 -3.92419602e-01  1.24915922e-01 -1.61420345e+01
  1.44769970e+01  1.75947216e+00 -5.65888405e+00  1.47860738e+01
 -2.22912479e+00  4.13006915e+00 -1.38190955e+00 -2.25659691e+00
 -1.58257308e+00 -1.22966326e+00 -2.07048458e+01  1.49033816e+01
 -3.93104898e+00  1.14133536e-01 -3.00209006e+00  8.22722821e-01
 -4.52690888e+00 -2.25885226e+00 -1.33250052e+00 -5.48638953e-01
  4.45576066e-01 -1.53147444e+01  1.33699177e+01 -1.51815182e+00
 -1.82260024e+00 -3.73762376e+00 -2.46850090e+00  7.90930662e-02
 -7.08640674e+00 -5.50042529e+00 -2.61026103e+00 -1.54035736e+00
 -2.46871089e+01  1.77814707e+01  1.94003527e+00  9.11322492e+00
 -3.34645669e+00 -4.48065173e+00  3.04599452e-02 -1.27892814e+00
 -1.44972239e+00 -1.40845070e+00 -1.58730159e-01 -2.70588235e+01
  3.61813426e+01 -3.32906530e+00  9.52536978e-01 -2.89787875e-01
  5.37084399e-01  2.34499665e+00 -1.67212744e-01 -1.58439148e-01
  1.40554510e-01 -7.00201481e+00 -2.07667965e+01  2.23386279e+01
 -1.65996986e+00  1.75438596e+00  7.56704981e+00  5.78806768e+00
  1.99214366e+00  1.76066025e+00  1.83833469e+00  3.37138306e+00
  1.87467899e+00 -1.08646332e+01  2.41798643e+01  4.73696197e+00
  1.77841865e+00  4.53364817e+00  3.81748362e+00  1.11836379e+01
  7.12328767e+00 -7.85531604e-01 -2.20953784e+00  2.48540764e+00
 -5.51166636e-02  3.75000000e+00  1.25797307e+00  4.96952649e+00
  4.15364002e+00  3.04459691e+00  1.87265918e+00 -1.63398693e-01
  4.90998363e-01 -4.51954397e+00 -1.10874200e+00 -4.74342389e-01
  7.36568458e+00 -4.35835351e+00  1.24300808e-01 -2.42085661e+00
  8.01526718e+00 -2.82685512e+00  5.57575758e+00  5.16647532e-01
 -4.39748715e+00  8.96057348e-01 -1.07164002e+01  1.25331565e+01
 -2.65173836e+00 -9.76764836e-01 -4.93199821e-01  3.98017423e+00
 -1.48779431e+00  1.17302053e-01 -2.21148213e+00 -1.21311966e+00
 -8.64160097e-01 -1.98195443e+01  1.63456037e+01  2.86885246e+00
 -2.89855072e-01  4.93291592e+00  3.79746835e+00  6.79559826e+00
  5.08670076e+00  5.10390751e+00  2.59337905e+00  1.59473398e-01
 -6.36234290e+01  1.35714286e+02  8.52988819e+00  1.92660550e+01
  2.19230769e+01  9.46372240e-01 -9.56250000e+01  9.79166667e+02
  7.79220779e+00  2.40963855e+00  8.06722689e+00 -3.18818040e+00
  5.62248996e+00  1.29277567e+01  5.95959596e+01  2.67088608e+01
  1.53513154e+01  3.91166282e+01 -6.10085080e+00  5.27599487e+01
  6.38655462e+01  2.01025641e+01  1.70794193e+00  2.01511335e+00
 -1.64609053e-01  1.07172300e+00 -6.11745514e-01 -2.99138285e+01
  3.75292740e+01 -3.40570456e-01  1.30522088e+00  6.42786351e+00
  1.84914194e+00  3.48746082e+00  1.83011486e+00  6.28408528e+00
  1.45772595e+00  8.94252874e+00 -3.82781177e+01  6.48547009e+01
  3.06926586e+00  6.58067913e-01  1.91161088e+01  4.97475302e+01
  2.25773347e+00  3.24014337e+00  9.15150674e+00  4.56743003e+00
 -2.37255141e+00 -1.46809571e+01  2.18083552e+01  3.80141504e+00
 -8.82160927e+00  1.82583013e+00  7.20752395e-01  3.75250894e-01
  2.15614676e+00  6.66382979e+00  2.55325940e+00 -2.56749397e+00
 -1.51800687e+01  1.80474487e+01  1.06388069e+01  1.27191194e+01
  4.33996383e-01 -5.04141160e-01 -2.35251538e+00  6.04151223e+00
  1.50297099e+00  1.06404959e+01  3.36134454e+00 -1.64107197e+01
  2.17939481e+01  8.60692103e+00 -2.24014337e+00  1.64986251e+00
  9.37781785e+00 -5.60593570e+00  4.89082969e+00 -1.08243131e+00
 -9.25925926e-01 -2.12404418e+00 -5.48611111e+01  1.26538462e+02
  1.10356537e+00  7.25229826e+00 -3.19047619e+01  5.46853147e+01
 -3.07414105e+00 -1.36194030e+01 -2.15982721e+00 -4.08388521e+00
 -3.56731876e+00 -5.01193317e+00  1.84673367e+01  3.28738070e+00
  2.79597675e+00  8.95870736e+00  4.86076784e+00 -1.19421747e+00
  9.36704835e+00  2.31932529e+00  2.30938677e+00 -1.40297264e+00
 -1.13623556e+01  1.66573949e+01  1.85979971e+00  3.08358818e+00
 -3.70828183e-01 -7.91563275e+00 -1.88628402e+00  1.11233178e+01
 -3.13890262e+00  6.12401123e+00 -2.50060111e+01 -1.00352677e+01
  1.75694939e+01  4.91057896e+00 -1.47192716e+01  2.40213523e+00
  1.98088619e+01  4.27846265e+00  1.27955494e+01  1.28236745e+01
  4.09836066e+00  1.83727034e+00 -2.04123711e+01  2.12435233e+01
  1.65598291e+00  1.33079848e+01  1.40939597e+01  1.05882353e+01
  4.25531915e+00  1.78571429e+00 -1.20300752e+01 -1.16809117e+01
 -1.45161290e+01 -1.88679245e+00  2.11538462e+01  1.58730159e+01
  1.21495327e+01  1.15476190e+01  1.06723586e-01  2.66524520e+00
  9.76116303e+00  3.97350993e+00  8.91719745e+00 -1.01921470e+01
 -3.64651163e+01  4.93411420e+01 -4.50980392e+00  1.87505634e+00
  3.62799752e-01  4.29377535e+00  6.08673599e+00  9.73782772e+00
  2.78120688e+00  5.00918468e+00  5.62470564e+00 -7.27434868e+00
  1.90149069e+01  4.08658009e+00 -9.63030580e+00 -7.07070707e-01
 -3.81485249e+00  2.00951877e+00  3.78434422e+00  1.89810190e+00
 -3.38235294e+00  9.23389143e+00 -2.25731537e+01  1.30173965e+01
  3.02547771e+00 -1.21140143e+01  2.70270270e+00 -6.57894737e+00
  2.95774648e+01  1.78260870e+01 -1.08856089e+01 -3.31262940e+01
 -8.04953560e+00 -2.32323232e+01  2.01754386e+01 -6.93430657e+00
 -2.04179104e+00 -1.27986348e+00  4.40795160e+00 -8.98770104e-01
  1.12768496e+01  7.50670241e-02  1.61808830e+00 -3.03701360e+00
 -2.96247961e+01  3.33951476e+01  8.10936052e-02 -3.50140056e+01
 -1.29310345e+01 -2.02970297e+01  3.35403727e+01  2.04651163e+01
  7.72200772e+00  1.43369176e+00 -7.77385159e+00 -1.80076628e+01
  9.34579439e+00  2.13675214e+00 -2.59696459e+00 -5.54016620e-01
  8.23467967e+00  4.18208139e-01 -3.60403652e+00  5.38384845e+00
 -4.58845790e+00  6.80879194e+00 -2.16927124e+01  2.67338471e+01
  6.04926723e+00 -5.71040109e+00  1.26171593e+01  2.76568502e+01
 -8.32497492e+00  1.33479212e+01  1.20656371e+01 -1.42118863e+00
 -9.48012232e+00 -5.98455598e+00  3.31108830e+01  7.98303124e+00
  9.82175336e-01  5.04322767e-01  1.73835125e+01  5.52671756e+00
  1.11689815e+01  1.44195731e+01  1.00090992e+00  1.13738739e+01
 -1.26188069e+01  5.27655635e+00 -3.20949659e+00  2.38439306e+00]
Predicted: [-1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841
  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841
  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841
  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841
  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491  3.75229782  4.71452724  5.67675666  6.63898607  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841
  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841
  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957
  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607
  7.60121549  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899
  2.79006841  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549
  8.56344491 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841
  3.75229782  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491
 -1.05884926 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782
  4.71452724  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926
 -0.09661985  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724
  5.67675666  6.63898607  7.60121549  8.56344491 -1.05884926 -0.09661985
  0.86560957  1.82783899  2.79006841  3.75229782  4.71452724  5.67675666
  6.63898607  7.60121549  8.56344491 -1.05884926]

Girls Growth:
Actual: [-1.03448276e+01 -8.97435897e+00  1.26760563e+01  1.25000000e+00
  3.20987654e+01  7.47663551e+00 -6.95652174e+00  4.67289720e+00
 -2.58928571e+01  3.61445783e+01  9.73451327e+00 -5.18617021e+00
  8.34502104e+00 -7.11974110e-01  8.08344198e+00 -2.11097708e+00
  7.88662970e+00  3.82638492e+00 -2.75027503e-01 -1.90843905e+01
  2.61077028e+01  3.13513514e+00  1.25628141e+00  8.85028950e+00
  9.57446809e+00  1.24826630e+00  1.23287671e+01  6.76829268e+00
  3.65505425e+00  8.26446281e+00 -2.05089059e+01  3.58514725e+01
  5.84354383e+00  1.96618168e+00  6.36328577e+00  2.97316896e+00
  5.80985915e+00  3.06156406e+00  8.87956087e+00  1.12692764e+00
 -3.46041056e+00 -2.14763062e+01  2.08897485e+01  3.16800000e+00
  4.01606426e+00  9.26640927e+00  7.42049470e+00  9.86842105e-01
  8.14332248e+00  6.92771084e+00  4.22535211e+00  8.10810811e-01
 -1.60857909e+00  2.80653951e+01 -2.76595745e+00 -2.31481481e+01
  2.04819277e+01 -2.00000000e+00  1.02040816e+00 -3.03030303e+00
  4.16666667e+00  3.90000000e+01  2.15827338e+00 -1.61971831e+01
  2.18487395e+01  1.72413793e+01 -2.89855072e+00  4.90405117e+00
  1.38211382e+01  2.00000000e+01  1.30952381e+01  1.44736842e+00
  4.28015564e+00  2.20149254e+01 -6.01427115e+00  3.03687636e+01
  1.10648918e+01  1.60000000e+00  7.87401575e+00  3.21167883e+01
  5.52486188e-01  2.14285714e+01  4.52488688e+00 -1.08225108e+01
  4.85436893e+00 -6.48148148e+00  2.37623762e+01  4.40000000e+00
 -4.89260143e+00 -2.82308657e+00 -1.61394448e-01  5.75493049e+00
  8.68236013e+00  8.94514768e+00  6.40330493e+00  5.82382917e+00
 -1.39646870e+01  2.97974414e+01  1.43737166e+00 -1.06649937e+00
  3.74128091e+00  5.03259984e+00  2.10475267e+00  1.57689750e+00
  1.51500982e+00  4.71672041e+00 -1.87384534e+00 -1.37170522e+01
  1.89941812e+01 -6.98567936e-02  6.06060606e-01 -7.53012048e-01
  3.92369391e+00  3.96328744e+00  2.42776886e+00 -1.25367287e+00
  4.46340012e+00  2.60159514e+00 -2.36165093e+01  2.76714320e+01
  1.70810400e+00  1.03542234e+01  5.67901235e+00  1.58878505e+01
  3.83064516e+00 -3.88349515e+00  2.42424242e+00  6.31163708e+00
  2.04081633e+00 -4.54545455e+00  2.03809524e+01  2.21518987e+00
  1.06557377e+01  7.40740741e+00 -1.31034483e+01  4.76190476e+00
 -5.30303030e+00  2.40000000e+01  9.03225806e+00 -9.46745562e+00
 -1.11111111e+01  1.83823529e+01  2.36024845e+01 -1.97183099e+00
 -1.72413793e+00  2.43664717e+00  3.13986679e+00  5.44280443e+00
  4.54943132e+00  1.09623431e+01  3.01659125e+00 -1.26647145e+01
  2.81642917e+01  1.53041203e+01 -9.57886045e+00  5.02283105e+00
 -1.21739130e+00  7.48239437e+00  1.06470106e+00  7.05024311e+00
  7.57002271e+00 -3.72976777e+00 -1.53508772e+01  1.96891192e+01
  5.26695527e+00  2.40740741e+01  7.46268657e-01 -3.70370370e+00
  5.38461538e+00 -1.09489051e+01  6.55737705e+00  7.69230769e-01
 -1.29770992e+01 -1.40350877e+01  2.24489796e+01  4.25000000e+01
 -2.66992203e+00  1.64266062e+00  1.96640395e+00  4.26296065e+00
  1.69237682e+00  2.23122239e+00  2.44903839e+00  1.18821627e+00
 -1.07003891e+00  7.99971906e+00 -1.43070820e-01 -4.14987913e+00
  2.35393022e+00 -4.92813142e-01 -2.88898060e-01  4.55298013e-01
  5.31520396e+00  6.06416275e+00  2.95094061e-01 -1.47480691e+01
  1.76013805e+01 -6.60308144e-01 -2.87707062e+00 -5.47576302e+00
  3.41880342e+00  2.47933884e+00  8.51254480e+00  3.79851363e+00
 -6.20525060e+00  6.53095844e+00 -2.77866242e+01  2.70121279e+01
  3.64583333e+00 -9.19439580e+00  6.65380906e+00 -9.04159132e-02
 -1.71945701e+00  1.93370166e+00  0.00000000e+00 -1.35501355e+00
 -7.32600733e-01 -1.44833948e+01  2.04962244e+01  4.65532677e+00
 -1.10552764e+01  3.38983051e+00  9.56284153e-01  0.00000000e+00
 -8.11907984e+00 -3.53460972e+00  2.74809160e+00 -2.08023774e+00
 -2.21547800e+01  2.86549708e+01  2.42424242e+00  6.18388934e+00
  1.07279693e+00 -4.85216073e+00  6.29482072e+00 -7.72113943e+00
 -1.54346060e+00 -2.47524752e+00  1.09983080e+00 -1.87447699e+01
  1.99794027e+01  2.48927039e+00 -1.24155560e+00 -1.20170087e+00
 -9.35628743e-02  6.12474246e+00  3.44158136e+00  2.23511346e+00
  7.04272363e+00 -1.02899906e+00 -1.62570888e+01  2.39653875e+01
  5.28072838e+00 -1.83276060e+00  3.15052509e+00  2.37556561e+00
  4.75138122e+00  1.37130802e+00  3.85015609e+00  7.51503006e+00
  5.59179870e-01 -1.27896200e+01  2.64612115e+01  1.76470588e+00
  4.65116279e-01  7.40740741e+00  6.55172414e+00  9.30420712e+00
  5.25536640e+00  2.67229255e+00  6.84931507e-01  4.62585034e+00
  4.74642393e+00  3.41402855e+00  7.80312125e-01  2.36406619e-01
  7.54716981e+00  1.97368421e+00  4.30107527e+00  8.24742268e-01
 -3.47648262e+00  6.77966102e+00  2.38095238e+00  3.29457364e+00
  8.44277674e+00 -1.21107266e+00  5.73065903e+00 -4.33604336e+00
  8.21529745e+00  6.02094241e+00  8.14814815e+00 -2.28310502e-01
  2.97482838e+00  1.77777778e+00 -1.39737991e+01  2.63959391e+01
  4.01606426e-01 -5.16406670e+00  2.72263188e+00  2.98177802e+00
  3.75335121e+00  1.55038760e+00  1.11959288e+00  1.30850528e+00
 -5.96125186e-01 -2.45377311e+01  3.44370861e+01  9.75369458e+00
  5.36992840e+00  3.34088335e+00  1.11780822e+01  1.27156235e+01
  1.00568430e+01  1.77195074e+01  8.80863989e+00  2.48138958e+00
 -6.01694915e+01  1.17021277e+02  1.31652661e+01  3.03030303e+00
 -2.05882353e+01  3.33333333e+01 -1.66666667e+01  1.16666667e+01
  5.97014925e+01  9.06542056e+01  1.37254902e+01  2.32758621e+01
  8.28671329e+01 -1.39579350e+01  4.70588235e+01  3.20000000e+01
  1.31313131e+01  1.83035714e+01  1.47169811e+01  2.30263158e+00
  8.68167203e+00  2.95857988e-01 -3.83480826e+01  5.45454545e+01
  1.36222910e+01  4.17690418e+00  4.71698113e-01  1.00938967e+01
  1.64179104e+01  8.05860806e+00  1.08474576e+01  4.35779817e+00
  7.17948718e+00 -3.55434040e+01  8.65323436e+01  1.00625355e+01
 -4.14012739e+00  1.32890365e+00  6.65573770e+01  5.31496063e+00
  8.41121495e+00  3.05172414e+01  9.24702774e-01  1.96335079e+00
 -1.86136072e+01  4.08517350e+01  1.87010078e+01 -3.19819820e+00
  2.04746394e+00  0.00000000e+00  3.73917009e+00  6.85714286e+00
  1.06129165e+01  3.68166605e+00  1.29124821e+00 -2.52832861e+01
  4.25592417e+01  9.40824468e+00  3.43642612e-01  5.99315068e+00
  1.24394184e+01 -3.16091954e+00  6.97329377e+00 -1.10957004e+00
  9.11640954e+00  9.76863753e+00 -2.42388759e+01  3.57032457e+01
  7.40318907e+00 -5.26315789e-01  0.00000000e+00  8.46560847e+00
  1.12195122e+01 -7.01754386e+00  5.66037736e+00 -1.78571429e+00
  9.09090909e+00 -3.75000000e+01  6.46666667e+01  2.10526316e+01
  8.33333333e+00  6.08695652e+01  1.08108108e+01  5.85365854e+01
  3.07692308e+00  2.23880597e+01 -9.75609756e+00 -3.24324324e+01
  4.80000000e+01  2.83783784e+01  3.89048991e+00  6.93481276e+00
  1.29701686e-01  4.66321244e+00  9.15841584e+00  8.04988662e+00
  4.40713536e+00  3.01507538e+00 -3.80487805e+00  3.72210953e+01
 -8.13008130e-01 -1.19904077e+00 -5.09708738e+00  9.20716113e+00
  1.77985948e+01  5.96421471e+00 -1.31332083e+00  4.37262357e+00
 -6.92167577e+00 -1.52641879e+01  2.97921478e+01  3.20284698e+00
 -2.17821782e+01  7.59493671e+00  3.88235294e+01  6.77966102e+00
  3.57142857e+01  2.45614035e+01  1.03286385e+01 -4.68085106e+00
 -2.36607143e+01  2.16374269e+01  4.32692308e+00  5.55555556e+00
  0.00000000e+00  4.73684211e+01  2.70270270e+00 -1.05263158e+01
  5.00000000e+01 -1.96078431e+00  6.66666667e+00  1.00000000e+01
  1.70454545e+00  9.49720670e+00  1.07142857e+01  2.11981567e+01
  9.12547529e+00 -6.27177700e+00 -3.42007435e+01  5.53672316e+01
 -2.90909091e+00 -7.96178344e-01  2.72873194e+00  5.62500000e+00
  7.54437870e+00  1.85694635e+01  4.75638051e+00  1.42857143e+01
  2.18023256e+01 -1.80588703e+01  3.59223301e+01  8.35714286e+00
 -1.46341463e+01 -7.85714286e+00  1.78294574e+01 -6.57894737e-01
 -5.96026490e+00  2.18309859e+01  1.73410405e+00  5.68181818e-01
 -1.75141243e+01  3.28767123e+01  8.24742268e+00  3.07692308e+01
  2.35294118e+01  3.17460317e+00 -4.00000000e+01 -2.30769231e+01
  2.35798499e+00  7.12041885e+00  1.25122190e+01  4.95221546e+00
  6.29139073e+00  9.81308411e+00  4.39716312e+00 -1.08695652e+00
 -2.18406593e+01  2.72407733e+01  5.04143646e+00 -2.80000000e+01
 -2.59259259e+01  1.50000000e+01  0.00000000e+00 -4.61538462e+01
 -1.42857143e+01 -7.28051392e+00  6.92840647e-01  5.50458716e+00
  5.86956522e+00  5.33880903e+00  2.47563353e+01  1.46875000e+01
  8.58310627e+00 -2.91091593e+01  3.75221239e+01  9.65250965e+00
  1.37931034e+01 -1.36363636e+01  7.01754386e+01 -1.13402062e+01
  2.20930233e+01  1.80952381e+01  1.12903226e+01 -1.44927536e+00
 -5.88235294e+00  5.46875000e+01  5.05050505e+00  8.39694656e+00
  4.22535211e+00  1.75675676e+01  1.83908046e+01  3.25242718e+01
  2.60073260e+01 -5.23255814e+00  5.21472393e+00 -1.22448980e+01
  2.72425249e+01  1.22715405e+01  3.04487179e+00  9.48678072e+00
  9.37500000e+00  9.61038961e+00  8.05687204e+00  7.45614035e+00
 -9.18367347e-01  9.37178167e+00 -2.33521657e+01  3.30466830e+01
  1.08033241e+01 -1.48743477e+00  2.32876712e+00  3.73940205e+00
  4.77747190e+00  3.77141606e+00  4.64579597e+00  4.35133262e+00
  1.56265095e+00 -1.62944995e+01  2.49623569e+01  3.73191165e+00]
Predicted: [3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549
 5.93570468 6.44255445 6.94940423 7.456254   7.96310378 8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549
 5.93570468 6.44255445 6.94940423 7.456254   7.96310378 8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549
 5.93570468 6.44255445 6.94940423 7.456254   7.96310378 8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549
 5.93570468 6.44255445 6.94940423 7.456254   7.96310378 8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 5.93570468 6.44255445 6.94940423 7.456254   8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549
 5.93570468 6.44255445 6.94940423 7.456254   7.96310378 8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549
 5.93570468 6.44255445 6.94940423 7.456254   7.96310378 8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558  3.90830557 4.41515535
 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423 7.456254
 7.96310378 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512
 5.4288549  5.93570468 6.44255445 6.94940423 7.456254   7.96310378
 8.46995355 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549
 5.93570468 6.44255445 6.94940423 7.456254   7.96310378 8.46995355
 3.4014558  3.90830557 4.41515535 4.92200512 5.4288549  5.93570468
 6.44255445 6.94940423 7.456254   7.96310378 8.46995355 3.4014558
 3.90830557 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445
 6.94940423 7.456254   7.96310378 8.46995355 3.4014558  3.90830557
 4.41515535 4.92200512 5.4288549  5.93570468 6.44255445 6.94940423
 7.456254   7.96310378 8.46995355 3.4014558 ]


Age Group: 19&over
Boys Growth:
Actual: [-6.16883117e+00 -5.53633218e+00  4.02930403e+00  1.76056338e+00
 -5.88235294e+00  2.57352941e+00  7.88530466e+00 -8.63787375e+00
 -3.89090909e+01  7.08333333e+01 -1.35888502e+01  2.85667378e+00
 -1.30885874e+00  8.08668931e-02 -3.16742081e+00  1.90253672e+00
 -6.14150016e+00  2.44285465e+00 -7.15380685e+00 -2.22711429e+01
  3.32074581e+01 -4.78384125e+00  1.48535565e+01 -1.78101599e+00
 -4.12116217e-02  8.53432282e+00 -1.04463438e+00 -6.67946257e+00
  1.09214315e+01 -3.74559614e+00 -4.08399152e+01  5.03419082e+01
  4.82997618e+00  1.93595818e+01  4.83164522e+00  5.75793184e+00
  2.32098765e+00  1.64092664e+00  1.78062678e-01 -1.06173717e+01
  1.93556940e+00 -5.00325140e+01  6.29099427e+01 -8.80332321e+00
 -7.82881002e+00  1.58550396e+00  1.37123746e+01 -4.70588235e+00
  7.92181070e+00 -5.43374643e+00 -2.61088710e+01 -1.17326057e+01
 -1.00463679e+01  3.60824742e+01 -6.18686869e+00 -2.17241379e+01
  7.04845815e+00  5.62414266e+00 -3.89610390e+00  1.52702703e+01
 -1.52403283e+00  7.73809524e+00 -3.42541436e+00 -6.97940503e+00
  9.84009840e+00  4.36730123e+00  3.70125578e+00 -2.29445507e+00
 -7.17547293e+00 -1.26493324e+00  4.19928826e+00 -1.96721311e+01
  1.52210884e+01  8.95940959e+01 -2.00856364e+01  3.27812957e+01
  7.22670580e+00 -2.65384615e+01  6.80628272e+00  4.70588235e+01
  2.63333333e+01  2.90237467e+00 -1.92307692e+01  0.00000000e+00
  1.20634921e+01  3.11614731e+00 -3.84615385e+00 -3.14285714e+00
 -6.00919053e-01  1.04196302e+01  3.92914654e+00  6.66253486e+00
  3.86403254e+00  4.02797203e+00 -7.26001613e-01 -7.01516793e+00
 -2.30702010e+01  2.20749716e+01  2.45037221e+00  8.46405229e+00
  5.96565230e+00 -1.70599943e+00 -1.30170668e+00  4.63071512e+00
 -5.32212885e+00  6.80473373e+00  8.31024931e-02 -2.71519513e+01
  2.90653495e+01  2.61995879e+00  5.16318507e-01 -3.86427898e-01
  2.28971520e+00 -1.32272685e+00  4.78065242e-01 -9.98227447e-01
  3.81643423e-01 -4.22905421e+00 -5.62046658e+01  3.29230081e+01
 -1.18706853e+00  1.43023935e+01  6.38406537e+00  5.42486798e+00
  4.23497268e+00  2.88335518e+00  2.25053079e+00 -4.15282392e-02
 -6.23182385e+00 -1.63048294e+01  2.30280572e+01 -3.52839931e+00
  9.36037441e-01  3.86398764e+00  5.50595238e+00 -4.23131171e-01
  2.26628895e+00  1.16343490e+01 -1.24069479e+00  6.28140704e-01
 -1.93508115e+01  2.56965944e+01  3.69458128e+00  4.50252581e+00
  1.34510298e+00  5.18457072e-01  3.11532907e+00  2.14085634e+00
  9.40254652e-01  5.02619833e+00 -2.64227642e+00 -2.15410894e+01
  2.45524915e+01 -5.24373665e-01  5.57413601e+00 -2.05913411e+00
  6.63072776e+00  4.17593529e+01  2.52139800e+01 -4.27228710e+00
  2.11246653e+00 -3.67132867e+00 -3.04597701e+01  1.78773380e+01
  6.34686347e+00 -5.52325581e+00  8.00000000e+00 -7.97720798e+00
 -1.23839009e+00  3.13479624e-01  2.18750000e+00 -1.65137615e+01
 -1.64835165e+01 -4.47368421e+01  9.04761905e+01 -8.33333333e-01
 -1.63276998e+00  3.52358765e+00  7.87623066e+00  1.70795306e+00
 -6.79400077e+00 -6.56030807e+00  1.11863409e+00 -8.15138282e-01
 -2.33783387e+01  2.35970121e+01  8.81760422e+00  3.87847447e+00
  2.48911014e-01  8.00744879e+00 -7.87356322e+00 -4.92825951e+00
 -4.26509186e+00  1.64496230e+00  2.49494268e+00 -4.84210526e+01
  5.33163265e+01  5.90682196e+00 -1.34920635e+01  1.25382263e+02
 -1.54002714e+01  1.76423416e+01 -5.65780504e+00 -4.84104046e+00
  3.41685649e+00 -1.23348018e+01 -2.65494137e+01  1.68757127e+01
 -2.13658537e+01 -7.31707317e+00  7.60233918e+00  5.29891304e+00
 -1.72903226e+01 -2.62090484e+01  4.22832981e-01  8.42105263e+00
 -3.49514563e+00 -3.03822938e+01  4.82658960e+01 -9.94152047e+00
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 -5.96199525e+01  9.00000000e+01  1.17647059e+01  8.80626223e+01
 -3.64203954e+00 -1.60907127e+01 -1.54440154e+00 -2.48366013e+00
  4.42359249e+00 -4.10783055e+00 -4.68540830e+00 -6.93820225e+01
  2.11467890e+02 -1.87039764e+01  7.75418809e+00 -5.77233895e-01
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 -3.70947978e+00  2.98780488e+00  9.53226761e+00  1.00540541e+01
  4.42043222e-01  2.78728606e+00  3.90104662e+00  3.84615385e+00
 -6.61375661e-01 -2.03728362e+01  2.71460424e+01  4.60324419e+00
 -1.74825175e+01  1.61016949e+01  2.55474453e+00  1.28113879e+01
  6.75078864e+01 -1.86440678e+01 -3.58796296e+01  1.51624549e+01
  2.75862069e+01  9.82800983e+00  1.07382550e+01  5.74412533e+00
  6.41975309e+00 -2.32018561e+00 -4.75059382e+00 -4.48877805e+00
  4.43864230e+00 -2.20000000e+01 -1.02564103e+01  3.57142857e+00
 -7.93103448e+00 -1.27340824e+01 -7.28476821e+00  7.14285714e-01
  5.91016548e+00 -9.15178571e+00  2.94840295e+00 -5.96658711e+00
 -1.52284264e+00 -1.23711340e+01 -3.11764706e+01  3.58974359e+01
 -1.63522013e+01 -6.84931507e-01  4.07523511e-01  2.68498283e+00
 -4.31742171e+00  2.00190658e+00 -8.72274143e-01 -2.04274041e+00
 -1.82868142e+00 -3.72549020e+01  3.08854167e+01  7.64027059e+00
 -2.04935777e+00  5.04641226e+00  5.54036047e-01  7.75561445e+00
  1.55339806e+00 -4.46144041e-01  2.39436620e+00 -6.00225084e-01
 -7.74877343e+01  3.01648505e+02  7.77043478e+00 -9.90099010e-01
  1.85000000e+01 -8.43881857e-01 -9.53191489e+01  1.06250000e+03
  5.32374101e+00 -2.73224044e-01  6.16438356e+00 -3.87096774e-01
  5.69948187e+00  1.04166667e+01  4.29522752e+01  8.77329193e+00
  1.22769450e+01  3.96058487e+01 -8.92531876e+00  1.95953757e+02
  1.42578125e+02  2.25442834e+01  1.44546649e+00  1.74870466e+00
 -2.60980267e+00 -1.30718954e-01 -2.22513089e+00 -3.64792503e+01
  5.97471022e+01 -5.73878628e+00  2.79181069e+00  7.78582514e+00
 -9.59923206e-02  3.38698054e+00  1.71933086e+00  8.42850617e+00
 -3.15989046e-01  1.20245139e+01 -5.42727787e+01  1.17244224e+02
  4.31067224e+00 -6.26174076e-02  3.86591479e+01  5.89245368e+01
 -1.13733295e-01  4.95303160e+00  5.20748576e+00  5.07862851e+00
 -2.15897939e+00 -1.30892678e+01  2.26485863e+01  7.29240179e+00
 -2.16643422e+01  5.87537092e+00 -8.40807175e-02 -1.96353436e+00
 -2.20314735e+00  1.14686951e+01  7.58530184e+00 -2.73237375e+00
 -3.42362679e+01  3.51639969e+01  1.65349887e+01  1.25902165e+01
  8.54700855e-01  3.53107345e-01 -8.30401126e+00  8.82578665e+00
  5.50070522e+00  1.27005348e+00  2.83828383e+00 -2.36200257e+01
  2.57142857e+01  1.24331551e+01  5.33980583e+00  2.25806452e+01
  1.03383459e+01 -1.00511073e+01  3.78787879e+00 -2.73722628e+00
  3.56472795e+00  1.99275362e+00 -8.52575488e+01  5.12048193e+02
  4.92125984e+00  2.04641350e+01 -2.73204904e+01  4.57831325e+01
 -3.63636364e+00 -4.18524871e+01 -1.50442478e+01 -1.66666667e+01
  2.08333333e+00  1.30612245e+01  7.94223827e+00  3.34448161e-01
  1.07803417e+01  1.17029734e+01  7.72953737e+00  1.28171247e+00
  8.41487280e+00  2.82791817e+00  1.57987127e+00 -7.02764977e-01
 -1.48973199e+01  1.90593047e+01  3.40089316e+00  5.65476190e+00
  9.06103286e+00 -1.71330176e+01 -3.89610390e+00  1.60000000e+01
 -3.82106244e+00  8.76937984e+00 -4.11135857e+01 -2.95763994e+01
  2.76047261e+01  0.00000000e+00  7.80487805e+00  6.78733032e+00
  2.75423729e+01  1.16279070e+00  8.21018062e+00  1.06221548e+01
  7.81893004e+00  4.32569975e+00 -2.12195122e+01  2.86377709e+01
  4.81347774e-01  3.06122449e+01  3.90625000e+01  1.68539326e+01
  6.73076923e+00 -2.20720721e+01 -2.25433526e+01 -3.43283582e+01
 -4.65909091e+01  4.25531915e+00  6.12244898e+00  4.61538462e+01
  1.83622829e+01  1.61425577e+01  3.61010830e-01 -3.77697842e+00
  2.42990654e+00  4.37956204e+00  5.76923077e+00 -9.75206612e+00
 -4.50549451e+01  7.23333333e+01 -3.48162476e+00  4.60580233e+00
  6.09526794e+00  6.35061291e+00  6.88793223e+00  5.72950500e+00
  1.19193905e+00  5.30661809e+00  3.96678967e+00 -6.51064774e+00
  1.63720489e+01  3.75165664e+00 -1.11607143e+01  8.79396985e+00
 -1.15473441e+01  2.61096606e-01  2.21354167e+01 -5.97014925e+00
 -1.38321995e+01  4.47368421e+01 -3.00000000e+01  2.12987013e+01
  2.84796574e+01 -2.22222222e+00  5.90909091e+00 -1.28755365e+00
  1.26086957e+01  1.93050193e+00 -8.33333333e+00 -3.47107438e+01
 -1.26582278e+00 -2.69230769e+01  2.71929825e+01 -1.24137931e+01
  7.41839763e+00  8.59422959e-01  7.69933049e+00  3.67335405e-01
  1.11204955e+01 -3.11629085e+00  5.80543933e+00 -6.00593178e+00
 -4.52800421e+01  8.26525709e+01 -2.89397527e+00 -1.84873950e+01
 -8.24742268e+00 -2.35955056e+01  2.64705882e+01  2.44186047e+01
  1.96261682e+01  5.46875000e+00 -7.40740741e+00 -1.60000000e+01
  8.57142857e+00  6.14035088e+00 -1.72830971e-01 -1.14265928e+00
  1.21541156e+01 -5.30918176e-01 -7.53532182e+00 -3.05602716e-01
 -1.53269755e+01  2.21238938e+00 -2.12121212e+01  2.74225774e+01
  2.66562132e+00 -4.04463040e+00  1.64244186e+01  4.70661673e+01
 -8.23429542e+00  1.61887142e+01  1.28980892e+01 -4.16078984e+00
 -1.15526122e+01 -4.24292845e+00  4.13553432e+01  9.15795943e+00
  1.65118679e+00  5.17766497e+00  4.35328185e+01  8.06993948e-01
  8.93929286e+00  5.93998775e+00  2.19653179e+00  3.24095023e+01
 -1.31567706e+01  2.70536153e+00 -5.36398467e+00  5.30016225e+00]
Predicted: [ 0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048
  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048
  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048
  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048
  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  6.59988301  7.80885555  9.01782808 10.22680061 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048
  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048
  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541
  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061
 11.43577314 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795
  5.39091048  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314
 12.64474568  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048
  6.59988301  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568
  0.55502035  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301
  7.80885555  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035
  1.76399288  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555
  9.01782808 10.22680061 11.43577314 12.64474568  0.55502035  1.76399288
  2.97296541  4.18193795  5.39091048  6.59988301  7.80885555  9.01782808
 10.22680061 11.43577314 12.64474568  0.55502035]

Girls Growth:
Actual: [-1.80327869e+01 -1.80000000e+01  9.75609756e+00 -8.88888889e+00
 -2.43902439e+00 -1.50000000e+01  5.88235294e+00  1.11111111e+01
 -7.00000000e+01  1.50000000e+02  0.00000000e+00 -4.01396161e+00
 -2.36363636e+00  2.23463687e+00 -3.46083789e+00 -3.39622642e+00
 -1.56250000e+00  6.15079365e+00  1.68224299e+00 -4.98161765e+01
  1.02197802e+02  1.26811594e+00  5.28455285e+00  2.23938224e+01
 -4.41640379e+00  0.00000000e+00  4.62046205e+00  7.57097792e+00
 -5.57184751e+00 -6.21118012e-01 -5.28125000e+01  1.24503311e+02
  6.48967552e+00  8.78378378e+00  7.29813665e+00  4.19681621e+00
  5.97222222e+00  1.83486239e+00  2.05920206e+00  3.27868852e+00
 -2.44200244e-01 -5.54467564e+01  1.13736264e+02  3.59897172e+00
  2.94117647e+01  2.27272727e+01 -2.46913580e+00 -1.26582278e+00
 -2.82051282e+01  2.32142857e+01 -2.89855072e+00 -1.49253731e+00
 -4.69696970e+01  6.57142857e+01  3.44827586e+00 -1.07142857e+01
  2.40000000e+01  3.22580645e+00 -9.37500000e+00  0.00000000e+00
  5.17241379e+01  3.18181818e+01 -5.17241379e+00 -3.09090909e+01
  3.15789474e+01  4.00000000e+00 -7.19424460e-01  1.23188406e+01
 -6.45161290e-01  9.09090909e+00 -2.38095238e+00  5.48780488e+00
 -6.35838150e+00 -1.23456790e+00  7.50000000e+00  3.37209302e+01
  1.43478261e+01 -4.66666667e+01  0.00000000e+00  2.25000000e+02
 -3.84615385e+00  3.20000000e+01  8.18181818e+01 -4.00000000e+01
 -2.77777778e+00 -3.14285714e+01  1.66666667e+01  7.14285714e+00
 -1.85185185e+00  3.94511149e+00  1.65016502e+00 -4.38311688e+00
  8.99830221e+00  5.45171340e+00  3.84047267e+00  1.29445235e+01
 -7.31738035e+01  2.17840376e+02  4.57902511e+00 -5.38876059e-01
  1.07585139e+01  8.24598183e+00  5.87475791e+00 -2.92682927e+00
 -4.45979899e+00  3.15581854e+00 -6.37348630e-02 -4.19005102e+01
  4.84083425e+01  2.66272189e+00  3.44629523e-01 -4.00686892e-01
  6.89655172e+00  3.01075269e+00 -2.45302714e+00 -3.26377742e+00
  5.69690265e+00 -6.80272109e-01 -4.04109589e+01  3.73121132e+01
  5.79523503e-01  1.14754098e+01 -2.50000000e+01  2.05882353e+01
 -9.75609756e+00  6.30630631e+00  1.52542373e+01 -5.88235294e+00
 -5.46875000e+00 -2.47933884e+01  5.71428571e+01 -6.99300699e-01
  2.36842105e+01  1.48936170e+01 -5.55555556e+00  1.37254902e+01
  5.17241379e+00  4.91803279e+00 -3.12500000e+00 -2.09677419e+01
  6.12244898e+00  1.15384615e+01  2.24137931e+01 -5.27704485e+00
 -2.22841226e+00 -4.27350427e+00  2.38095238e+00 -1.16279070e+00
 -9.11764706e+00  1.22977346e+01  1.72910663e+00 -4.50424929e+01
  5.46391753e+01  1.53333333e+01 -8.21917808e+00 -6.46766169e+00
  1.06382979e+00  1.15789474e+01  2.35849057e+00  5.99078341e+00
  1.04347826e+01 -8.26771654e+00 -2.96137339e+01  3.84146341e+01
  1.76211454e+00  4.16666667e+01  4.11764706e+01 -2.50000000e+01
  6.66666667e+01 -4.66666667e+01 -1.25000000e+01  7.14285714e+00
  6.00000000e+01 -5.83333333e+01  1.50000000e+02  1.28000000e+02
  7.24907063e+00  8.89659157e+00  1.22015915e+00  1.62473795e+00
  4.12583806e-01 -1.38674884e+00  1.40625000e+00  2.56805342e-01
 -3.44262295e+01  4.76562500e+01  7.03703704e+00 -1.31991051e+01
 -1.28865979e+00 -1.56657963e+01 -8.04953560e+00  1.68350168e+00
 -3.31125828e-01 -6.97674419e+00 -6.42857143e+00 -5.61068702e+01
  9.91304348e+01  1.09170306e+01 -6.07734807e+00  5.88235294e+00
  5.55555556e-01  6.62983425e+00 -1.91709845e+01  4.48717949e+00
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 -7.40740741e+00 -3.17269076e+01  1.17647059e+01  1.57894737e+00
 -1.65803109e+01 -1.55279503e+01  1.17647059e+01  4.60526316e+00
 -3.14465409e+00 -5.97402597e+01  8.06451613e+01  3.21428571e+01
 -1.21428571e+01  6.50406504e+00  6.10687023e+00 -1.94244604e+01
 -6.25000000e+00  1.90476190e+00  9.34579439e+00 -3.41880342e+00
 -5.57522124e+01  1.24000000e+02  3.57142857e+00  4.38356164e+01
  0.00000000e+00 -1.80952381e+01  1.82170543e+01 -3.63934426e+01
 -1.28865979e+01 -3.55029586e+00 -1.10429448e+01 -6.48275862e+01
  1.82352941e+02 -1.38888889e+00  1.85873606e-01  3.52504638e+00
 -3.67383513e+00 -3.44186047e+00  2.40847784e+00 -1.41110066e+00
  1.59351145e+01 -1.56378601e+01 -5.24878049e+01  9.05544148e+01
  1.75646552e+01  2.23214286e-01  2.67260579e+00  8.67678959e-01
  3.44086022e+00  3.53430353e+00  6.02409639e-01  9.58083832e+00
  3.64298725e-01 -2.28675136e+01  3.12941176e+01 -3.40501792e+00
 -2.35294118e+01  3.84615385e+01  2.03703704e+01 -9.23076923e+00
  8.47457627e+00  2.03125000e+01 -2.98701299e+01 -3.70370370e+00
  3.84615385e+01 -2.36111111e+01  2.72727273e+01  2.08333333e+01
  2.41379310e+01 -2.22222222e+01 -1.78571429e+01 -3.91304348e+01
  7.14285714e+01 -2.91666667e+01 -2.94117647e+01 -3.33333333e+01
  8.75000000e+01  2.00000000e+01  3.54838710e+01 -9.52380952e+00
 -3.50877193e+00  4.54545455e+00 -8.69565217e-01  3.50877193e+00
  7.62711864e+00 -7.08661417e+00 -6.27118644e+01  1.31818182e+02
  6.86274510e+00 -3.49946978e+00  3.51648352e+00 -1.16772824e+00
  5.37056928e-01  4.27350427e-01 -5.31914894e-01  2.13903743e+00
 -2.82722513e+00 -4.79525862e+01  6.33540373e+01  1.76172370e+01
  1.02536998e+01  1.62991371e+00  1.88679245e-01  9.79284369e+00
 -3.25900515e+00  4.96453901e+00  4.22297297e+00 -4.29497569e+00
 -7.97629128e+01  3.44769874e+02  1.15710254e+01  4.50000000e+01
 -3.44827586e+00  1.07142857e+01 -2.58064516e+01  1.73913043e+01
  1.00000000e+02  5.92592593e+01 -1.39534884e+01  1.08108108e+01
  1.12195122e+02 -2.06896552e+01  6.15384615e+01  4.16666667e+01
  1.84873950e+01  9.21985816e+00  1.29870130e+00 -9.61538462e+00
  1.48936170e+01  1.85185185e+00 -5.09090909e+01  6.91358025e+01
  3.72262774e+01  1.03896104e+01 -2.94117647e+00  7.47474747e+00
 -1.50375940e+00 -7.63358779e-01  8.84615385e+00 -1.76678445e-01
  1.64601770e+01 -5.66869301e+01  1.67368421e+02  1.60104987e+01
  8.33333333e-01 -1.65289256e+00  6.55462185e+01 -1.52284264e+00
  1.34020619e+01 -4.54545455e-01 -4.10958904e+00 -5.71428571e+00
 -5.55555556e+00  3.31550802e+01  2.97188755e+01 -2.97984224e+00
  3.52303523e+00 -1.65794066e+00 -2.21827862e+00 -2.72232305e-01
  8.37124659e+00  8.64819479e+00 -1.62287481e+00 -5.10604870e+01
  9.50240770e+01  4.52674897e+00  5.76271186e+00  6.41025641e+00
  6.02409639e+00 -7.38636364e+00  4.60122699e+00 -8.79765396e-01
  8.87573964e-01  1.52492669e+01 -4.45292621e+01  5.91743119e+01
  2.16138329e+01  0.00000000e+00 -1.38888889e+00  7.04225352e+00
 -3.94736842e+00 -2.32876712e+01  1.42857143e+01  1.56250000e+00
 -1.53846154e+00 -6.87500000e+01  2.30000000e+02  4.84848485e+01
  4.44444444e+01  1.80000000e+02  7.14285714e+00 -4.66666667e+01
 -1.25000000e+01  5.71428571e+01 -9.09090909e+00 -4.00000000e+01
  1.00000000e+02  5.00000000e+01  7.65171504e+00  6.86274510e+00
 -1.07798165e+01  4.37017995e+00 -4.92610837e+00  6.73575130e+00
 -4.85436893e+00  1.02040816e+00 -2.47474747e+01  4.42953020e+01
 -2.55813953e+00 -4.44444444e+00 -2.32558140e+00  1.42857143e+01
  1.00000000e+01  3.03030303e+00 -1.47058824e+00  5.59701493e+00
 -9.18727915e+00 -4.39688716e+01  7.50000000e+01  6.74603175e+00
 -1.19047619e+01 -2.70270270e+00  8.33333333e+00  1.02564103e+01
 -6.97674419e+00  2.75000000e+01  3.13725490e+01  2.98507463e+00
 -1.88405797e+01  1.25000000e+01  3.17460317e+00 -4.44444444e+01
  2.00000000e+01  8.33333333e+01 -2.00000000e+01 -2.50000000e+01
  6.66666667e+01  4.00000000e+01  5.12820513e+00  6.09756098e+00
 -3.44827586e+00 -4.76190476e+00 -2.25000000e+01  2.09677419e+01
  4.00000000e+00  1.28205128e+01 -4.20454545e+01  7.84313725e+01
  2.85714286e+01 -5.83090379e+00  1.11455108e+01  3.34261838e+00
  2.42587601e+00 -4.47368421e+00  5.50964187e+00  1.04438642e+01
  1.01654846e+01 -1.93133047e+01  4.28191489e+01  1.24767225e+01
 -2.00000000e+01 -1.78571429e+00  9.09090909e+00 -3.33333333e+00
 -1.20689655e+01 -7.84313725e+00  1.27659574e+01 -1.13207547e+01
 -3.82978723e+01  6.55172414e+01  2.91666667e+01 -1.50000000e+01
 -5.29411765e+01  7.50000000e+01 -5.00000000e+01  4.28571429e+01
  9.11854103e-01  4.51807229e+00  1.72910663e+01 -1.96560197e+00
  1.25313283e+00  8.16831683e+00  2.74599542e+00 -6.01336303e+00
 -3.81516588e+01  6.47509579e+01  8.37209302e+00 -1.42857143e+01
  3.33333333e+01  0.00000000e+00  0.00000000e+00 -5.00000000e+01
  1.00000000e+02 -1.51785714e+01  0.00000000e+00  6.31578947e+00
 -9.90099010e-01 -9.00000000e+00  1.64835165e+00  1.35135135e+01
  4.28571429e+00 -2.19178082e+01  2.16374269e+01  4.32692308e+00
  2.22222222e+01 -4.54545455e+00  8.57142857e+01  2.56410256e+00
 -5.00000000e+00  1.05263158e+01  7.14285714e+00  8.88888889e+00
 -1.83673469e+01  7.50000000e+01  8.57142857e+00  5.58823529e+01
 -1.88679245e+00  2.50000000e+01  1.23076923e+01 -9.58904110e+00
  3.93939394e+01  1.08695652e+01  1.17647059e+01 -1.22807018e+01
  6.00000000e+00 -9.43396226e-01  5.06329114e+00  7.63052209e+00
  4.47761194e+00  2.50000000e+00 -7.31707317e+00  7.51879699e+00
 -4.54545455e+00  1.90476190e+01 -4.36923077e+01  6.83060109e+01
  1.81818182e+01  1.32153322e+00  4.12535411e+00  2.42589129e+00
  1.46090421e+00 -1.60894464e+00  1.84035477e+00  4.06052689e+00
 -4.60299194e-01 -4.52390962e+01  7.29968333e+01  8.32039050e+00]
Predicted: [ 3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476  7.52614831
  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476  7.52614831
  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476  7.52614831
  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476  7.52614831
  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   8.61132186  9.69649541 10.78166896 11.8668425  14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476  7.52614831
  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476  7.52614831
  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766  5.35580121
  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425
 12.95201605 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476
  7.52614831  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605
 14.0371896   3.18545411  4.27062766  5.35580121  6.44097476  7.52614831
  8.61132186  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896
  3.18545411  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186
  9.69649541 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411
  4.27062766  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541
 10.78166896 11.8668425  12.95201605 14.0371896   3.18545411  4.27062766
  5.35580121  6.44097476  7.52614831  8.61132186  9.69649541 10.78166896
 11.8668425  12.95201605 14.0371896   3.18545411]


Age Group: 17-18
Boys Growth:
Actual: [-2.69841270e+01  5.43478261e+00  1.03092784e+01 -2.52336449e+01
  1.12500000e+01  6.74157303e+00 -1.89473684e+01 -3.89610390e+00
 -2.70270270e+00 -4.16666667e+00 -1.15942029e+01 -5.37634409e-01
 -5.94594595e+00 -2.15517241e-01  2.95176386e+00 -4.68531469e+00
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 -2.22222222e+01  3.21428571e+01  5.40540541e+00  3.58974359e+01
  2.26415094e+01  3.84615385e+01 -2.44444444e+01  2.35294118e+01
  4.76190476e+01 -2.98507463e+00 -1.53846154e+00  1.40625000e+01
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  3.30578512e+00 -2.00000000e+00 -9.10931174e+00]
Predicted: [-3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
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 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711
  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166
  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
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  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585
  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
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  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166
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  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
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  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
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  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166
  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
  1.41877585  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462
  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585
  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711
  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166
  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
  1.41877585  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462
  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585
  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711
  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166
  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
  1.41877585  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462
  9.35421838  2.74134961  4.06392336  6.70907087 -3.87151916 -2.54894541
 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711
  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166
  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
  1.41877585  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462
  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585
  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
 -1.22637166  0.0962021   1.41877585  2.74134961  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541  0.0962021   1.41877585
  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
 -1.22637166  0.0962021   1.41877585  4.06392336  5.38649711  8.03164462
  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585
  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961
  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
  1.41877585  2.74134961  4.06392336  6.70907087  8.03164462 -3.87151916
 -2.54894541 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336
  5.38649711  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541
 -1.22637166  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711
  6.70907087  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166
  0.0962021   1.41877585  2.74134961  4.06392336  5.38649711  6.70907087
  8.03164462  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021
  1.41877585  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462
  9.35421838 -3.87151916 -2.54894541 -1.22637166  0.0962021   1.41877585
  2.74134961  4.06392336  5.38649711  6.70907087  8.03164462  9.35421838
 -3.87151916]

Girls Growth:
Actual: [-6.66666667e+01  1.00000000e+02  0.00000000e+00 -8.33333333e+01
  7.00000000e+02 -1.25000000e+01 -4.28571429e+01  0.00000000e+00
  0.00000000e+00  7.50000000e+01 -1.42857143e+01 -1.49253731e+00
  6.06060606e+00 -5.00000000e+00 -7.51879699e+00  8.13008130e-01
  3.70967742e+01 -1.00000000e+01 -6.53594771e+00  5.59440559e+00
  5.29801325e+00  1.50943396e+01 -1.57894737e+01 -1.56250000e+00
  1.90476190e+01 -4.00000000e+00  2.50000000e+01 -1.11111111e+01
  6.25000000e+00  5.88235294e+00 -8.88888889e+00  5.00000000e+01
  2.92682927e+01 -1.78571429e+00  3.03030303e+00  1.05882353e+01
  1.06382979e+00  5.26315789e+00 -1.10000000e+01  1.57303371e+01
 -3.39805825e+00  1.00502513e+01  1.05022831e+01 -1.65289256e+00
 -1.36363636e+01  2.63157895e+01 -2.91666667e+01  2.94117647e+01
  9.09090909e+00 -5.00000000e+01  3.33333333e+01  1.87500000e+01
 -3.15789474e+01  3.07692308e+01 -2.94117647e+01  0.00000000e+00
  1.00000000e+02 -5.00000000e+01  1.00000000e+02 -3.75000000e+01
 -6.00000000e+01  0.00000000e+00  2.00000000e+02 -1.66666667e+01
 -4.00000000e+01  6.66666667e+01  7.27272727e+01  0.00000000e+00
  4.73684211e+01 -2.85714286e+01  0.00000000e+00  1.00000000e+01
  9.09090909e+00  6.66666667e+01  7.50000000e+00  0.00000000e+00
  1.62790698e+01  1.33333333e+02 -2.85714286e+01  1.00000000e+02
  5.00000000e+01  1.33333333e+01 -2.35294118e+01  0.00000000e+00
 -2.30769231e+01 -3.00000000e+01  1.28571429e+02  5.00000000e+01
 -1.74418605e+00 -2.54437870e+01 -2.53968254e+01 -8.51063830e+00
  9.30232558e+00  1.27659574e+01  8.49056604e+00  1.30434783e+01
  1.23076923e+01  1.36986301e+00 -1.08108108e+01 -8.24742268e-01
 -2.07900208e-01  4.79166667e+00  1.29224652e+01 -4.04929577e+00
  1.11926606e+01  8.74587459e+00  1.21396055e+00 -2.09895052e+00
  1.42419602e+01 -4.82573727e+00  1.59362550e+00  4.31372549e+00
  3.38345865e+00 -2.54545455e+00  5.59701493e+00 -5.30035336e+00
  1.49253731e+00  1.47058824e+00 -1.66666667e+01  3.69565217e+01
  6.03174603e+00  3.75000000e+01 -4.54545455e+00  1.42857143e+01
  2.08333333e+01 -4.13793103e+01  5.88235294e+00  2.22222222e+01
 -1.81818182e+01 -5.55555556e+00  6.47058824e+01  3.92857143e+01
  6.00000000e+01 -1.87500000e+01 -1.53846154e+01 -4.54545455e+01
 -5.00000000e+01 -3.33333333e+01  5.00000000e+01  6.66666667e+01
 -6.00000000e+01  5.00000000e+01  1.66666667e+02 -2.98507463e+00
 -1.53846154e+01 -5.45454545e+00 -5.76923077e+00  1.02040816e+01
  3.33333333e+01 -1.38888889e+01 -1.61290323e+01  1.92307692e+01
  4.67741935e+01 -6.59340659e+00 -6.81818182e+00 -1.21951220e+00
 -2.71604938e+01  1.18644068e+01  4.09090909e+01 -1.93548387e+01
  6.66666667e+00  1.25000000e+01 -2.55555556e+01  2.98507463e+01
 -1.37931034e+01  1.66666667e+02  0.00000000e+00  1.12500000e+02
 -6.47058824e+01 -8.33333333e+01  2.00000000e+02  0.00000000e+00
  1.00000000e+02 -5.00000000e+01 -3.33333333e+01  5.00000000e+02
 -5.16304348e+00  4.29799427e+00  7.69230769e+00  3.31632653e+00
  3.95061728e+00 -2.61282660e+00 -1.21951220e+00  7.40740741e+00
  2.89655172e+01 -2.13903743e+01 -9.52380952e+00 -4.42477876e+00
  3.70370370e+00  1.78571429e+00 -1.75438596e+00 -1.78571429e+00
  1.81818182e+01 -3.07692308e+00  7.93650794e+00  1.47058824e+01
  3.20512821e+00 -3.10559006e+00  8.77192982e+00 -4.51612903e+01
  6.17647059e+01 -1.45454545e+01  2.34042553e+01 -1.72413793e+01
 -2.70833333e+01  5.71428571e+00  8.10810811e+01 -1.19402985e+01
 -3.22033898e+01  5.52631579e+01  1.18644068e+01  2.42424242e+01
 -7.31707317e+00 -3.94736842e+00  9.58904110e+00 -2.50000000e+01
 -2.16666667e+01  3.19148936e+01 -3.22580645e+00  3.50000000e+01
  4.82758621e+01 -3.25581395e+01  4.48275862e+01  1.19047619e+01
 -3.19148936e+01 -2.81250000e+01 -1.73913043e+01 -2.10526316e+01
  8.00000000e+01  4.44444444e+01  2.30769231e+01 -2.81250000e+01
 -1.73913043e+01  1.31578947e+01 -1.39534884e+01 -2.70270270e+00
 -2.77777778e+00  2.28571429e+01  9.30232558e+00  4.25531915e+00
  0.00000000e+00  2.04081633e+00  1.83823529e+00 -5.41516245e+00
 -7.25190840e+00  1.56378601e+01  2.49110320e+00  3.47222222e-01
  3.80622837e+00  3.33333333e+00  1.32258065e+01 -4.27350427e+00
  0.00000000e+00  9.52380952e+00 -1.52173913e+01  3.33333333e+01
 -5.76923077e+00 -3.26530612e+01  6.06060606e+01 -1.32075472e+01
 -8.69565217e+00  0.00000000e+00  1.19047619e+01 -1.06382979e+01
 -3.17460317e+00  1.63934426e+00 -2.09677419e+01  3.06122449e+01
 -7.81250000e+00 -1.18644068e+01  3.84615385e+01 -5.55555556e+00
 -1.76470588e+01  3.57142857e+00  2.58620690e+01  1.81818182e+01
 -1.34615385e+01 -8.88888889e+00  7.31707317e+00  0.00000000e+00
 -4.54545455e+00  2.38095238e+00  1.39534884e+01  2.04081633e+00
 -4.00000000e+00  2.08333333e+00  2.27272727e+01  3.70370370e+01
 -8.10810811e+00  2.94117647e+00 -2.00000000e+01  1.07142857e+01
 -1.61290323e+01 -7.69230769e+00  1.66666667e+01  5.00000000e+01
 -1.90476190e+01  5.88235294e+00 -1.25000000e+01  3.17460317e+00
 -1.53846154e+00  3.12500000e+00 -3.03030303e+00  7.81250000e+00
 -1.30434783e+01 -1.83333333e+01  3.06122449e+01  3.28125000e+01
 -6.06060606e+00 -8.06451613e+00  3.33333333e+01  1.05263158e+01
  1.90476190e+01  1.30000000e+01  2.65486726e+00 -3.44827586e+00
  8.92857143e-01  6.19469027e+00  0.00000000e+00 -5.71428571e+01
 -1.00000000e+02  0.00000000e+00  2.00000000e+02  6.66666667e+01
  0.00000000e+00  4.00000000e+01  4.28571429e+01  1.00000000e+01
  0.00000000e+00 -1.11111111e+01  0.00000000e+00  3.75000000e+01
 -1.81818182e+01 -1.11111111e+01 -2.50000000e+01  3.33333333e+01
 -3.75000000e+01  1.60000000e+02  8.46153846e+01 -1.17647059e+01
 -6.66666667e+00 -3.57142857e+00  2.22222222e+01 -3.03030303e+00
  1.56250000e+01  5.40540541e+00  2.82051282e+01 -1.60000000e+01
  5.23809524e+01 -1.25000000e+01  2.22222222e+01  0.00000000e+00
 -9.09090909e+00  2.00000000e+01 -8.33333333e+00  5.00000000e+01
 -1.51515152e+01  1.42857143e+01  2.81250000e+01 -2.92682927e+01
  4.48275862e+01 -1.62790698e+01 -8.33333333e+00  1.01010101e+01
 -8.25688073e+00 -2.00000000e+00  8.16326531e+00 -6.60377358e+00
  1.71717172e+01  0.00000000e+00 -1.03448276e+01  4.13461538e+01
 -3.70370370e+00 -2.30769231e+01  5.50000000e+01 -3.87096774e+01
  8.42105263e+01  1.14285714e+01 -2.56410256e+01 -1.72413793e+01
  3.33333333e+01  9.37500000e+00  0.00000000e+00  3.75000000e+01
 -9.09090909e+00 -2.00000000e+01 -2.50000000e+01  1.16666667e+02
 -3.84615385e+01  3.75000000e+01  9.09090909e+00 -4.16666667e+01
  8.57142857e+01  1.53846154e+01  1.00000000e+02  0.00000000e+00
  5.00000000e+01 -6.66666667e+01  0.00000000e+00 -1.00000000e+02
  1.00000000e+02  5.00000000e+01 -1.87500000e+01 -1.53846154e+01
  6.81818182e+01 -2.70270270e+01 -1.48148148e+01  3.47826087e+01
  0.00000000e+00  3.87096774e+01 -2.32558140e+00  2.14285714e+01
 -7.84313725e+00 -2.50000000e+01 -6.66666667e+00  5.00000000e+01
  6.66666667e+01  5.71428571e+00 -2.16216216e+01 -1.03448276e+01
  3.46153846e+01  2.85714286e+01 -2.88888889e+01 -6.25000000e+00
  3.33333333e+01 -7.50000000e+01  1.00000000e+02  1.00000000e+02
  7.50000000e+01 -4.28571429e+01  2.50000000e+01 -6.00000000e+01
  1.00000000e+02  5.00000000e+01 -5.00000000e+01 -6.66666667e+01
  0.00000000e+00  0.00000000e+00  0.00000000e+00 -1.00000000e+02
  2.00000000e+01  0.00000000e+00  0.00000000e+00 -1.66666667e+01
  4.00000000e+01 -4.28571429e+01  5.00000000e+01  1.66666667e+01
  2.85714286e+01  3.33333333e+01 -2.50000000e+01  2.72727273e+01
 -7.14285714e+00 -2.30769231e+01  1.00000000e+01  5.45454545e+01
 -1.47058824e+01  2.41379310e+01  1.94444444e+01 -2.32558140e+00
  4.28571429e+01  5.00000000e+00 -4.00000000e+01  0.00000000e+00
  0.00000000e+00  0.00000000e+00 -1.66666667e+01  6.00000000e+01
  7.50000000e+01 -2.85714286e+01  3.00000000e+01  0.00000000e+00
  0.00000000e+00 -1.00000000e+02  1.00000000e+02  5.00000000e+01
 -6.66666667e+01 -1.17647059e+01 -1.50000000e+01  1.56862745e+01
  4.91525424e+01 -2.61363636e+01  2.76923077e+01  1.44578313e+01
  3.26315789e+01 -2.38095238e+01  4.16666667e+00  2.30000000e+01
 -1.00000000e+02 -5.00000000e+01 -1.00000000e+02 -1.00000000e+02
 -5.00000000e+00 -2.10526316e+01 -1.33333333e+01  3.07692308e+01
  1.17647059e+01 -2.10526316e+01  1.00000000e+02  3.33333333e+00
 -9.67741935e+00  1.78571429e+01  9.09090909e+00  6.66666667e+01
 -2.00000000e+01 -2.50000000e+01  3.33333333e+01 -2.50000000e+01
  6.66666667e+01 -8.00000000e+01  0.00000000e+00  4.00000000e+02
  1.00000000e+02  2.00000000e+01 -2.00000000e+01  2.50000000e+01
  1.00000000e+02  1.00000000e+01  0.00000000e+00 -9.09090909e+00
  2.00000000e+01 -4.16666667e+01  4.28571429e+01  1.10000000e+02
  4.28571429e+01 -4.65116279e+01  3.47826087e+01  4.83870968e+01
  2.17391304e+00  2.76595745e+01 -1.16666667e+01 -2.07547170e+01
  3.57142857e+01 -3.50877193e+00 -3.63636364e+00  5.66037736e+00
 -1.39318885e+00 -3.83045526e+00  4.99510284e+00  3.82462687e+00
  2.27613058e+00  2.89897511e+00  2.41889585e+00  4.16782440e+00
  4.50786877e+00  7.63144461e+00  2.91676547e+00]
Predicted: [ 4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 11.82748238 12.99966518
 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 12.99966518 14.17184797 15.34403077
 16.51621356  4.79438562  5.96656841  7.13875121  8.310934    9.4831168
 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 11.82748238 12.99966518
 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 12.99966518 14.17184797 15.34403077
 16.51621356  4.79438562  5.96656841  7.13875121  8.310934    9.4831168
 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 11.82748238 12.99966518
 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 12.99966518 14.17184797 15.34403077
 16.51621356  4.79438562  5.96656841  7.13875121  8.310934    9.4831168
 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 11.82748238 12.99966518
 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 12.99966518 14.17184797 15.34403077
 16.51621356  4.79438562  5.96656841  7.13875121  8.310934    9.4831168
 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 11.82748238 12.99966518
 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 12.99966518 14.17184797 15.34403077
 16.51621356 10.65529959 11.82748238 14.17184797  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 11.82748238 12.99966518
 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 12.99966518 14.17184797 15.34403077
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 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  8.310934    9.4831168
 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  11.82748238 12.99966518 15.34403077
 16.51621356  4.79438562  5.96656841  7.13875121  8.310934    9.4831168
 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562  5.96656841  7.13875121  8.310934    9.4831168  10.65529959
 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 14.17184797 15.34403077  4.79438562
  5.96656841  7.13875121  8.310934    9.4831168  10.65529959 11.82748238
 12.99966518 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841
  7.13875121  8.310934    9.4831168  10.65529959 11.82748238 12.99966518
 14.17184797 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121
  8.310934    9.4831168  10.65529959 11.82748238 12.99966518 14.17184797
 15.34403077 16.51621356  4.79438562  5.96656841  7.13875121  8.310934
  9.4831168  10.65529959 11.82748238 12.99966518 14.17184797 15.34403077
 16.51621356  4.79438562  5.96656841  7.13875121  8.310934    9.4831168
 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356
  4.79438562]


Age Group: 15-16
Boys Growth:
Actual: [-2.36111111e+01 -1.81818182e+00  9.25925926e-01  1.83486239e+00
 -6.30630631e+00  9.61538462e-01 -1.90476190e+00 -1.35922330e+01
  2.24719101e+00 -1.09890110e+00  3.33333333e+00 -7.47126437e-01
 -3.47423277e-01 -4.64846020e+00 -3.41255332e+00  4.79495268e+00
 -3.25105358e+00 -3.92034848e+00  1.87823834e+00 -6.86586141e+00
  1.36518771e+00 -7.40740741e-01 -1.89258312e+00 -3.44108446e+00
  6.20950324e+00  1.93187595e+00  1.84538653e+00  2.88932419e+00
 -8.04378867e+00 -4.76190476e+00 -5.59782609e+00  7.48416811e-01
 -9.14285714e-01 -4.01221108e+00  3.18037256e+00  5.10788199e+00
  4.14746544e+00  4.70635559e+00  7.91394545e+00  1.03239587e+00
 -1.76180409e+00  2.25968436e+00 -1.08733778e+00 -2.26950355e+00
 -1.22881356e+01  1.93236715e+00  9.47867299e+00  3.46320346e+00
  6.69456067e+00 -7.45098039e+00  5.08474576e+00  9.27419355e+00
 -2.21402214e+00 -1.13207547e+00  1.71755725e+01 -7.14285714e+00
 -5.76923077e+00  1.02040816e+01  9.25925926e-01 -2.47706422e+01
  7.31707317e+00  9.09090909e+00  2.08333333e+01 -6.89655172e+00
 -1.20370370e+01  2.10526316e+00 -2.13980029e+00 -5.68513120e+00
  4.94590417e+00 -4.12371134e+00  0.00000000e+00  8.75576037e+00
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  1.80451128e+01  1.14649682e+01 -4.00000000e+00 -1.07142857e+01
  1.66666667e+01  9.14285714e+00  4.71204188e+00  8.00000000e+00
  1.23152709e+00 -1.94647202e+00 -5.70719603e+00  6.57894737e+00
  1.35802469e+00  1.94884287e+00  1.43369176e+00 -9.42285041e-01
  2.49702735e+01 -1.56041865e+01  7.32807215e+00  3.12056738e-01
  2.63009050e+00  6.88895012e+00  4.79505027e+00  1.27921279e+00
  1.82171484e+00  4.79484733e+00 -9.78829957e-01 -3.54022989e+00
  3.36034318e+00  6.68664976e-01 -3.66165200e+00 -6.06953447e+00
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 -9.98697351e-01 -1.28898129e+01 -1.19331742e+00  7.00483092e+00
  5.41760722e+00 -1.92719486e+00  0.00000000e+00  5.24017467e+00
  1.24481328e+00 -1.22950820e+00 -6.43153527e+00  2.68292683e+01
  8.84955752e-01 -2.01754386e+01  2.08791209e+01  5.45454545e+00
 -1.81034483e+01 -1.05263158e+01  2.35294118e+01  4.76190476e+00
 -1.72727273e+01  3.29670330e+00  6.38297872e+00 -3.17604356e+00
  8.43486410e-01  4.27509294e+00 -4.54545455e+00  5.22875817e+00
  1.77462289e-01  2.03720106e+00 -3.29861111e+00  3.05206463e+00
  3.31010453e+00  5.56492411e+00  3.85220126e+00 -1.96820590e+00
  5.09652510e+00  4.11462160e+00 -1.48200423e+00 -4.01146132e+00
  5.44776119e+00  1.55697098e+00 -5.78397213e+00  1.99704142e+00
 -1.74039159e+00 -9.33333333e+00 -1.47058824e+01  0.00000000e+00
  0.00000000e+00  5.17241379e+00  6.55737705e+00  1.84615385e+01
  3.89610390e+00 -1.25000000e+00 -1.26582278e+00 -1.41025641e+01
  4.15713196e+00  1.50128158e+00  3.03030303e+00  2.31092437e+00
  1.98494182e+00  3.12080537e+00 -5.53205337e-01 -1.07984293e+00
  1.65398611e+01 -8.45869997e+00  3.34883721e+00  4.71311475e+00
 -1.66340509e+00  1.99004975e+00  2.43902439e+00  2.28571429e+00
  1.86219739e-01  2.32342007e+00  1.36239782e+00 -1.07526882e+00
  7.24637681e-01 -8.99280576e-02 -1.31696429e+01 -8.22622108e+00
  2.63305322e+01  4.87804878e+00 -7.82241015e+00 -6.88073394e+00
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 -9.30787589e+00 -5.59322034e+00 -1.23877917e+01 -1.02459016e+00
  4.96894410e+00 -4.53648915e+00  7.64462810e+00  4.79846449e+00
  5.67765568e+00  5.19930676e-01  3.10344828e+00  1.67224080e-01
 -6.25000000e+00 -4.84848485e+00  8.91719745e+00  0.00000000e+00
  1.16959064e+00 -7.22543353e+00  4.04984424e+00 -1.79640719e+00
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  1.19760479e+01  5.34759358e+00 -5.48546352e-02  2.19538968e+00
  6.95488722e+00  1.63193573e+00 -3.23616601e+00 -2.55297421e+00
  1.75530521e+00  7.72399588e-01 -1.86509964e+00 -1.79640719e+00
 -3.34040297e+00 -5.59006211e+00 -1.11842105e+01  1.18518519e+01
  1.19205298e+01 -1.71597633e+01 -7.14285714e-01  8.63309353e+00
  8.60927152e+00  1.52439024e+01  1.21693122e+01  8.01886792e+00
  1.98606272e+01 -1.16279070e+01  5.59210526e+00  0.00000000e+00
 -8.09968847e+00  1.38983051e+01  2.97619048e-01  1.78041543e+00
  1.74927114e+01  9.42928040e+00  2.04081633e+00  1.25714286e+01
 -5.07614213e-01  6.63265306e+00  1.29186603e+01  2.11864407e+00
 -3.31950207e+00 -8.58369099e+00  7.51173709e+00  8.29694323e+00
  1.29032258e+01  1.78571429e+00  1.46788991e+01 -1.60000000e+00
 -2.43902439e+00  3.33333333e+00  1.69354839e+01 -2.75862069e+00
 -4.25531915e+00 -9.62962963e+00  4.09836066e+00  2.20472441e+01
  1.16129032e+01 -5.66037736e+00 -2.57142857e+00  6.45161290e+00
 -9.91735537e+00  4.89296636e+00  1.74927114e+00  6.87679083e+00
  6.70241287e+00  5.52763819e+00 -2.61904762e+00  1.95599022e+00
  6.25000000e-01  1.04037267e+01  2.10970464e+00  5.30303030e+00
  3.07390451e+00 -3.23604061e+00  3.47540984e+00 -8.87198986e-01
 -1.74552430e+01  1.91324555e+01  5.33159948e+00  0.00000000e+00
  2.85714286e+01 -1.00000000e+02  1.50000000e+02  3.07692308e+01
 -1.17647059e+01  1.77777778e+01  1.88679245e+01  3.65079365e+01
 -8.13953488e+00  3.03797468e+01  1.74757282e+01  2.14876033e+01
  6.05442177e+01  1.44067797e+01 -1.14942529e+00  4.65116279e+00
 -4.44444444e+00  2.09302326e+01  9.61538462e+00 -1.75438596e+00
  1.87500000e+01  6.76691729e+00 -2.18309859e+01 -3.60360360e+00
  1.40186916e+01  4.87106017e+00  3.27868852e+00  1.05820106e+00
  7.85340314e+00  2.42718447e+00  5.21327014e+00 -1.80180180e+00
 -9.17431193e-01 -7.63888889e+00  1.05263158e+01  6.57596372e+00
  7.85907859e+00 -3.01507538e+00  1.06217617e+01  1.03044496e+01
  5.73248408e+00 -3.21285141e+00  2.48962656e+00  5.06072874e+00
  4.23892100e+00  4.25138632e+00  5.31914894e+00 -2.03417413e+00
 -3.23920266e+00 -5.06437768e+00 -5.42495479e-01  6.54545455e+00
  3.83959044e+00 -6.57354150e-01 -9.09842845e-01 -3.42237062e+00
  2.42005186e+00  9.62025316e+00  2.33333333e+01  1.35135135e+01
 -1.09523810e+01 -2.03208556e+01 -2.68456376e+00  4.82758621e+00
  1.97368421e+01  1.37362637e+01  6.28019324e+00 -7.72727273e+00
  8.86699507e+00 -9.78260870e+00 -1.20481928e+01  1.91780822e+01
 -1.14942529e+00  1.16279070e+00 -1.49425287e+01 -1.89189189e+01
  4.16666667e+01 -9.41176471e+00  0.00000000e+00 -3.89610390e+00
 -1.37254902e+01  4.09090909e+01  1.61290323e+00 -4.76190476e+00
 -8.33333333e+00 -3.63636364e+00  1.69811321e+01 -1.77419355e+01
  2.15686275e+01  1.29032258e+01 -8.41013825e+00 -2.26415094e+00
 -4.11840412e+00 -5.23489933e+00  5.38243626e+00 -6.18279570e+00
  1.57593123e+00  7.05218618e+00  1.31752306e-01  1.11842105e+01
  2.48520710e+00  1.13013699e+01  3.38461538e+00 -7.44047619e+00
  1.92926045e+00  7.88643533e+00  9.64912281e+00  6.13333333e+00
 -7.78894472e+00 -1.08991826e+00  1.01928375e+01  1.90000000e+01
 -4.32000000e+01  7.04225352e+00  3.94736842e+00 -7.59493671e+00
  6.84931507e+00  2.30769231e+01  3.12500000e+00  8.08080808e+00
 -1.77570093e+01  9.09090909e+00  1.42857143e+01 -1.87500000e+01
  3.07692308e+01  5.88235294e+01  1.11111111e+01 -1.00000000e+01
 -7.40740741e+00 -4.40000000e+01  3.57142857e+01  1.57894737e+01
 -4.54545455e+00  8.12500000e+01  3.44827586e+00 -6.66666667e+00
  1.78571429e+01  6.66666667e+01 -9.09090909e+00 -2.00000000e+01
  1.25000000e+01  3.11111111e+01 -2.20338983e+01 -2.17391304e+00
 -7.42996346e+00 -4.86842105e+00  4.70262794e+00  1.05680317e+00
  5.09803922e+00 -1.11940299e+00 -2.51572327e-01  6.17906683e+00
  4.63182898e+00  1.38479001e+01  7.47756730e+00 -3.78787879e+00
 -4.85829960e+00  1.82978723e+01  2.51798561e+00 -7.71929825e+00
 -3.04182510e+00  1.17647059e+00 -1.16279070e+01  4.82456140e+00
 -4.18410042e-01 -6.89655172e+00 -5.92592593e+01 -9.09090909e+00
  4.00000000e+01 -3.57142857e+01  5.55555556e+01  5.00000000e+01
  4.00000000e+01 -1.90476190e+01 -1.70157068e+00  5.45938748e+00
  8.08080808e+00 -2.80373832e+00 -1.56250000e+00  0.00000000e+00
  5.49450549e+00 -7.52314815e+00  3.75469337e-01 -3.86533666e+00
 -5.88235294e+01  0.00000000e+00 -4.28571429e+01 -4.16666667e+01
  7.14285714e+01 -4.16666667e+01  2.85714286e+01  4.44444444e+01
 -3.07692308e+01  5.55555556e+01  7.14285714e+00 -9.86547085e+00
  1.99004975e+00  3.17073171e+00  1.13475177e+01 -1.29511677e+01
 -1.70731707e+00  8.43672457e+00  6.86498856e-01 -4.77272727e+00
  1.95704057e+01  7.98403194e-01  4.31034483e+00 -1.81818182e+01
 -5.05050505e+00  1.59574468e+01  7.33944954e+00 -2.56410256e+00
 -4.38596491e+00  1.46788991e+01 -1.36000000e+01  4.62962963e+00
  2.65486726e+00 -2.94117647e+00  3.03030303e+00  1.17647059e+01
 -5.26315789e+00 -1.04166667e+00  1.19298246e+01 -5.95611285e+00
  5.00000000e+00  4.44444444e+00 -5.47112462e+00  8.03858521e+00
 -6.09756098e+00]
Predicted: [-0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344
  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995
 -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344
  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995
 -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344
  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995
 -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344
  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995
 -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995   2.8942888   3.48389147  4.07349415  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344
  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995
 -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344
  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995
 -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682 -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809  1.12548077
  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682
  5.2526995  -0.64332726  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  4.07349415
  4.66309682  5.2526995  -0.05372458  0.53587809  1.12548077  1.71508344
  2.30468612  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995
 -0.64332726 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612
  2.8942888   3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726
 -0.05372458  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888
  3.48389147  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458
  0.53587809  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147
  4.07349415  4.66309682  5.2526995  -0.64332726 -0.05372458  0.53587809
  1.12548077  1.71508344  2.30468612  2.8942888   3.48389147  4.07349415
  4.66309682  5.2526995  -0.64332726]

Girls Growth:
Actual: [ 0.00000000e+00 -8.33333333e+01  3.00000000e+02  1.25000000e+02
 -2.22222222e+01  1.42857143e+01  0.00000000e+00  0.00000000e+00
 -1.25000000e+01  0.00000000e+00  4.28571429e+01 -4.89130435e+00
 -4.57142857e+00  1.85628743e+01  2.57575758e+01 -9.63855422e+00
 -1.51111111e+01  2.46073298e+01 -2.94117647e+00 -1.29870130e+01
  2.18905473e+01  8.16326531e-01 -2.60869565e+00  5.35714286e+00
  3.72881356e+01  6.17283951e-01 -1.16564417e+01  4.86111111e+00
 -1.05960265e+01  2.96296296e+01  1.42857143e+01  2.70000000e+01
 -2.75590551e+00 -2.38095238e+00  6.91056911e+00  3.80228137e+00
  8.42490842e+00 -5.06756757e+00  9.60854093e+00  1.78571429e+01
  9.09090909e+00 -1.46464646e+01 -5.02958580e+00  6.54205607e+00
 -3.20000000e+01  5.29411765e+01  3.07692308e+01 -2.94117647e+01
 -1.66666667e+01  2.50000000e+01 -1.20000000e+01 -1.36363636e+01
 -1.57894737e+01  4.37500000e+01  5.65217391e+01 -9.09090909e+00
 -1.00000000e+01  0.00000000e+00 -3.33333333e+01  0.00000000e+00
  0.00000000e+00 -1.66666667e+01  2.00000000e+01  6.66666667e+01
  2.00000000e+01  8.33333333e+00 -2.60869565e+01 -1.47058824e+01
 -6.89655172e+00  1.48148148e+01  3.54838710e+01  2.85714286e+01
  2.03703704e+01 -1.53846154e+01  2.90909091e+01  1.69014085e+01
  9.63855422e+00  0.00000000e+00  2.20000000e+02  5.00000000e+01
 -3.33333333e+01  6.25000000e+00 -2.94117647e+01  3.33333333e+01
  5.62500000e+01  1.20000000e+01  1.42857143e+01 -3.12500000e+01
 -6.27802691e+00 -2.67942584e+01 -7.84313725e+00  1.13475177e+01
  7.00636943e+00  1.84523810e+01  7.53768844e+00 -9.34579439e+00
  1.80412371e+01 -1.31004367e+00  1.63716814e+01  1.17424242e+01
  1.14124294e+01 -7.09939148e-01  6.12870276e-01  7.61421320e+00
  0.00000000e+00  2.45283019e+00  5.61694291e+00  1.74367916e+00
 -1.19965724e+00 -3.12228968e+00  5.03978780e+00 -8.08080808e+00
  9.89010989e+00  6.75000000e+00 -1.12412178e+01 -3.16622691e+00
 -1.00817439e+01  1.03030303e+01 -8.51648352e+00  1.17117117e+01
  2.68817204e+00  2.38095238e+01  3.07692308e+01 -1.76470588e+01
 -2.14285714e+01  1.36363636e+01  3.20000000e+01 -1.51515152e+01
  5.71428571e+01 -2.27272727e+00  3.02325581e+01  0.00000000e+00
  0.00000000e+00 -3.33333333e+01  0.00000000e+00 -1.00000000e+01
  0.00000000e+00 -2.22222222e+01  0.00000000e+00  0.00000000e+00
 -1.42857143e+01  3.33333333e+01 -1.25000000e+01 -2.08791209e+01
 -2.77777778e+00  2.28571429e+01  3.48837209e+00 -1.12359551e+00
  3.40909091e+00  7.69230769e+00 -4.08163265e+00  1.38297872e+01
 -8.41121495e+00  1.22448980e+01 -2.00000000e+01  1.42857143e+01
  1.04166667e+01  3.77358491e+00  1.81818182e+00  1.33928571e+01
 -1.25984252e+01  7.20720721e+00 -1.17647059e+01  1.71428571e+01
  4.06504065e+00  1.33333333e+02 -4.28571429e+01 -7.50000000e+01
  1.50000000e+02  4.00000000e+01  4.28571429e+01 -3.00000000e+01
 -1.42857143e+01  6.66666667e+01  4.00000000e+01 -4.28571429e+01
 -3.42105263e+00 -4.90463215e+00  9.74212034e+00 -1.43603133e+00
  1.19205298e+00 -6.54450262e+00  5.46218487e+00  4.24966799e+00
  4.77707006e+01 -2.45689655e+01  1.14285714e-01 -6.97674419e+00
 -2.50000000e+00 -4.10256410e+00  9.09090909e+00  8.33333333e+00
  7.23981900e+00  1.39240506e+01  9.25925926e+00 -1.35593220e+00
 -6.18556701e+00  1.46520147e+00 -1.92307692e+01  1.30952381e+01
  1.36842105e+01 -1.01851852e+01 -1.34020619e+01  5.95238095e+00
 -1.91011236e+01  2.50000000e+01  6.66666667e+00 -1.87500000e+01
  8.97435897e+00  2.52525253e+01 -4.83870968e+00 -5.08474576e+00
  7.14285714e+00 -1.08333333e+01 -1.02803738e+01  1.14583333e+01
 -3.73831776e+00  7.76699029e+00 -1.17117117e+01  3.06122449e+00
  3.72093023e+01  2.88135593e+01 -1.18421053e+01 -5.97014925e+00
 -3.96825397e+01 -1.05263158e+01  2.94117647e+00  1.71428571e+01
  4.39024390e+01  3.38983051e+00  1.80327869e+01 -1.76470588e+01
 -1.57142857e+01 -1.01694915e+01  1.88679245e+00  9.25925926e+00
  2.20338983e+01  5.55555556e+00 -2.63157895e+00  8.10810811e+00
 -3.00000000e+01  3.03571429e+01 -2.59179266e+00  4.65631929e+00
  1.05932203e+01  1.72413793e+00 -4.51977401e+00  4.93096647e+00
  4.51127820e+00 -2.69784173e+00 -3.32717190e+00 -3.05927342e+00
  5.32544379e+00  8.77192982e+00  8.06451613e+00 -3.28358209e+01
  2.88888889e+01  1.55172414e+01 -1.94029851e+01 -3.70370370e+00
  3.84615385e+00  0.00000000e+00  5.55555556e+00  1.92982456e+01
 -1.73469388e+01  3.95061728e+01 -9.73451327e+00  3.92156863e+00
  2.92452830e+01 -1.45985401e+00 -1.48148148e+01 -1.91304348e+01
  1.61290323e+01  1.38888889e+01  1.86991870e+01 -1.25000000e+01
  1.42857143e+01 -6.25000000e+00 -3.33333333e+00  1.72413793e+00
  1.35593220e+01  1.19402985e+01  0.00000000e+00 -1.06666667e+01
  2.98507463e+00  1.44927536e+00 -7.14285714e+00  2.05128205e+01
 -4.25531915e+00 -2.22222222e+01 -8.57142857e+00  1.87500000e+01
  2.36842105e+01  6.38297872e+00 -1.00000000e+01 -4.44444444e+00
  6.97674419e+00 -2.23140496e+01  1.48936170e+01 -1.01851852e+01
 -2.06185567e+00  8.42105263e+00  5.82524272e+00  6.42201835e+00
  6.89655172e+00 -1.12903226e+01  1.36363636e+01 -1.60000000e+00
  5.26315789e+00  1.10000000e+01  2.79279279e+01  2.04225352e+01
 -2.33918129e+00  4.19161677e+00  7.47126437e+00  1.12299465e+01
 -2.45192308e+01  2.99363057e+01  1.56862745e+01 -1.66666667e+01
  4.00000000e+01 -8.57142857e+01  2.00000000e+02  6.66666667e+01
  1.00000000e+02  2.00000000e+01  8.33333333e+00  1.76923077e+02
 -5.55555556e+00  1.25000000e+01  2.22222222e+01 -9.09090909e+00
  0.00000000e+00 -4.00000000e+01  5.00000000e+01  5.55555556e+01
  5.00000000e+01  2.38095238e+01 -1.53846154e+01  0.00000000e+00
 -5.12820513e+00  5.40540541e+00  7.69230769e+00  1.42857143e+01
  3.12500000e+01 -1.58730159e+00 -1.61290323e+00  1.14754098e+01
 -8.82352941e+00  6.12903226e+01  1.60000000e+01 -1.53846154e+01
 -1.21212121e+01  1.72413793e+01 -1.47058824e+01  3.10344828e+01
  3.15789474e+01  4.00000000e+00  7.69230769e+00 -7.14285714e+00
  4.80769231e+01  1.55844156e+01  8.45070423e+00 -6.49350649e+00
 -4.16666667e+00  3.62318841e+00  8.39160839e+00  1.22580645e+01
 -5.17241379e+00 -8.48484848e+00  1.92052980e+01  3.05555556e+01
  2.12765957e+00  1.56250000e+01 -1.08108108e+01  4.54545455e+01
  2.29166667e+01 -8.47457627e+00 -9.25925926e+00 -2.04081633e+00
  2.29166667e+01  1.69491525e+00 -6.66666667e+00  3.57142857e+00
 -3.33333333e+01  2.00000000e+01  8.33333333e+00  5.38461538e+01
 -2.00000000e+01 -1.87500000e+01  7.69230769e+00  2.14285714e+01
  1.17647059e+01 -1.05263158e+01  2.94117647e+01  0.00000000e+00
  2.00000000e+02 -1.66666667e+01 -4.00000000e+01 -1.00000000e+02
  3.00000000e+02  5.00000000e+01 -1.66666667e+01 -2.00000000e+01
  3.94736842e+01 -9.43396226e+00 -1.25000000e+01  1.19047619e+01
 -4.25531915e+00  8.88888889e+00  1.22448980e+01  5.45454545e+00
  2.58620690e+01  1.78082192e+01  3.48837209e+00  3.91304348e+01
  1.56250000e+01  2.16216216e+01 -1.33333333e+01  3.07692308e+01
  9.80392157e+00  2.14285714e+01 -2.64705882e+01 -2.20000000e+01
  1.53846154e+01  2.44444444e+01 -5.71428571e+01  1.33333333e+02
 -1.42857143e+01 -1.66666667e+01 -4.00000000e+01  1.66666667e+02
 -1.25000000e+01  5.71428571e+01 -6.36363636e+01 -5.00000000e+01
  4.50000000e+02  1.00000000e+02 -5.00000000e+01  1.00000000e+02
  2.00000000e+02 -1.00000000e+02  2.00000000e+02 -3.33333333e+01
  6.66666667e+01 -2.00000000e+01  0.00000000e+00 -5.00000000e+01
  5.00000000e+01  6.66666667e+01  8.00000000e+01 -1.66666667e+01
 -1.33333333e+01  6.92307692e+01 -2.77777778e+01  1.92307692e+01
  4.19354839e+01  2.27272727e+00  6.66666667e+00  2.70833333e+01
  8.19672131e+00  6.06060606e+00  1.71428571e+01  1.09756098e+01
  1.53846154e+01 -4.16666667e+01 -2.85714286e+01  8.00000000e+01
  2.22222222e+01  6.36363636e+01  4.44444444e+01 -1.92307692e+01
 -2.38095238e+01  1.25000000e+01  2.77777778e+01  0.00000000e+00
  2.00000000e+02  3.33333333e+01  0.00000000e+00 -1.00000000e+02
 -7.95454545e+00  2.22222222e+01  2.02020202e+01  1.09243697e+01
  9.09090909e+00  6.25000000e+00  7.18954248e+00 -6.09756098e+00
  0.00000000e+00  9.74025974e+00  5.32544379e+00 -5.00000000e+01
 -1.00000000e+02 -1.00000000e+02  3.33333333e+01  2.08333333e+01
  2.41379310e+01 -3.05555556e+01  1.60000000e+01  5.17241379e+01
 -6.81818182e+00  4.14634146e+01 -1.20689655e+01  1.17647059e+01
  1.22807018e+01  8.00000000e+01 -5.55555556e+01  0.00000000e+00
 -2.50000000e+01  1.33333333e+02 -2.85714286e+01  1.00000000e+02
 -2.00000000e+01  7.50000000e+01  7.14285714e+00  1.33333333e+01
  2.66666667e+02  3.63636364e+01  0.00000000e+00 -6.66666667e+00
  3.57142857e+01 -2.10526316e+01  2.66666667e+01 -5.26315789e+00
  0.00000000e+00  1.22222222e+02 -2.50000000e+00  4.61538462e+01
  0.00000000e+00  5.26315789e+00  3.16666667e+01 -1.51898734e+01
  7.46268657e+00  1.38888889e+01 -1.34146341e+01 -9.85915493e+00
  4.68750000e+01  2.55319149e+01 -3.54330709e-01  2.54839984e+00
  5.50953573e+00  3.50556874e+00  7.76150997e-01  2.62559076e+00
  4.16169197e+00  3.92991649e+00  5.27808413e+00  1.51152350e+00
  4.64396285e+00]
Predicted: [14.97507311 14.19748287 13.41989263 12.6423024  11.86471216 11.08712192
 10.30953169  9.53194145  8.75435121  7.97676098  7.19917074 14.97507311
 14.19748287 13.41989263 12.6423024  11.86471216 11.08712192 10.30953169
  9.53194145  8.75435121  7.97676098  7.19917074 14.97507311 14.19748287
 13.41989263 12.6423024  11.86471216 11.08712192 10.30953169  9.53194145
  8.75435121  7.97676098  7.19917074 14.97507311 14.19748287 13.41989263
 12.6423024  11.86471216 11.08712192 10.30953169  9.53194145  8.75435121
  7.97676098  7.19917074 14.97507311 14.19748287 13.41989263 12.6423024
 11.86471216 11.08712192 10.30953169  9.53194145  8.75435121  7.97676098
  7.19917074 14.97507311 14.19748287 13.41989263 12.6423024  11.86471216
 11.08712192 10.30953169  9.53194145  8.75435121  7.97676098  7.19917074
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  7.97676098  7.19917074 14.97507311]


Age Group: 13-14
Boys Growth:
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  3.33333333e+00 -3.41555977e+00  9.03732809e+00 -7.38738739e+00
  1.30350195e+01  1.06712565e+01 -5.88235294e+00  8.92857143e+00
  5.73770492e+00  0.00000000e+00 -1.24031008e+01  7.07964602e+00
  1.15702479e+01 -2.22222222e+00 -7.57575758e-01  2.21374046e+01
  3.75000000e+00  2.56410256e+01  0.00000000e+00  0.00000000e+00
  2.38095238e+00 -5.98006645e+00  3.53356890e-01  1.47887324e+01
  3.06748466e+00  5.95238095e-01  1.53846154e+01 -1.02564103e+01
 -3.92156863e+00]
Predicted: [1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104
 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344
 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906
 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104
 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344
 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906
 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104
 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344
 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906
 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104
 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344
 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906
 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104
 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 2.91378104 3.14175864 3.36973625 3.59771385 4.05366906
 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104
 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344
 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906
 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104
 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.36973625 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344
 2.91378104 3.14175864 3.36973625 3.82569145 4.05366906 1.77389302
 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864
 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063
 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625
 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823
 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385
 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583
 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145
 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344
 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906
 1.77389302]

Girls Growth:
Actual: [ 5.00000000e+02  0.00000000e+00  1.66666667e+01 -1.42857143e+01
  8.33333333e+01 -9.09090909e+00  0.00000000e+00  0.00000000e+00
 -2.00000000e+01  5.00000000e+01  2.50000000e+01 -1.60427807e+01
  3.37579618e+01  8.57142857e+00 -1.88596491e+01  7.02702703e+00
  1.41414141e+01 -9.73451327e+00  1.37254902e+01 -1.29310345e+01
  5.44554455e+00  1.12676056e+01  1.66666667e+01  9.71428571e+00
  1.40625000e+01 -2.28310502e+00 -1.30841121e+01  8.60215054e+00
  1.93069307e+01  1.32780083e+01 -7.32600733e+00  4.74308300e+00
 -2.26415094e+00  7.98611111e+00  3.53697749e+00  0.00000000e+00
  8.69565217e+00  7.42857143e+00  9.04255319e+00  3.17073171e+00
 -1.32387707e+01 -4.08719346e+00  7.10227273e+00  7.95755968e-01
  2.08333333e+01 -3.44827586e+00  7.14285714e+00  2.66666667e+01
 -2.10526316e+01 -6.66666667e+00  3.57142857e+00 -3.44827586e+00
  3.92857143e+01  3.58974359e+01  1.50943396e+01 -1.00000000e+01
 -1.11111111e+01  0.00000000e+00  3.75000000e+01 -2.72727273e+01
 -3.75000000e+01  2.00000000e+02  1.33333333e+01 -5.88235294e+00
  2.50000000e+01  1.50000000e+01 -4.70588235e+01  3.70370370e+01
  2.43243243e+01  3.91304348e+01  6.25000000e+00 -1.47058824e+00
  1.34328358e+01  2.89473684e+01  0.00000000e+00  2.44897959e+01
  2.29508197e+01  3.33333333e+01 -1.50000000e+01  3.52941176e+01
 -8.69565217e+00 -1.42857143e+01  7.22222222e+01  6.45161290e+00
 -3.03030303e+00 -6.25000000e+00  3.33333333e+01 -7.50000000e+00
 -1.82767624e+00  6.64893617e+00  4.98753117e-01 -4.46650124e+00
 -7.79220779e-01  0.00000000e+00 -2.61780105e-01  3.67454068e+00
  1.72151899e+01  1.61987041e+01  2.60223048e+00 -4.00320256e-01
  8.27974277e+00  5.12249443e+00  2.11864407e+00  0.00000000e+00
  1.03734440e+00 -5.47570157e-01 -2.89057123e+00  3.54358611e-01
  3.74293785e+00  1.36147039e+00  5.01193317e+00  6.59090909e+00
 -8.52878465e-01 -6.02150538e+00 -2.05949657e+00  9.34579439e-01
  0.00000000e+00  6.71296296e+00 -1.38828633e+01  1.48614610e+01
  8.11403509e+00 -1.15384615e+01 -1.95652174e+01  1.62162162e+01
 -2.32558140e+01  4.54545455e+01  2.91666667e+01 -8.06451613e+00
  3.50877193e+01 -1.29870130e+00  5.26315789e+00 -1.12500000e+01
  2.00000000e+01  0.00000000e+00 -1.66666667e+01 -1.00000000e+01
 -1.11111111e+01  0.00000000e+00  6.25000000e+01  3.07692308e+01
 -4.70588235e+01  2.22222222e+01  4.54545455e+01  1.44444444e+01
  1.26213592e+01  0.00000000e+00  8.62068966e-01 -8.54700855e-01
  1.29310345e+01  6.10687023e+00  0.00000000e+00 -3.59712230e+00
  1.71641791e+01  2.61146497e+01  3.47826087e+00  2.52100840e+00
  1.31147541e+01  1.01449275e+01 -9.86842105e+00  1.02189781e+01
 -1.19205298e+01  1.72932331e+01  5.12820513e+00  2.43902439e+00
  1.66666667e+01 -1.00000000e+01  0.00000000e+00  2.22222222e+01
  2.72727273e+01 -5.00000000e+01  1.42857143e+01  7.50000000e+01
  3.57142857e+01 -1.57894737e+01 -3.75000000e+01  7.00000000e+01
  8.04953560e-01  1.59705160e+00 -2.66021765e+00  2.67080745e+00
 -2.17785844e+00 -3.58688930e+00  1.79602309e+00 -2.20541903e+00
  1.66237113e+01 -6.62983425e-01  1.16796440e+00  5.82191781e+00
  2.26537217e+00  2.84810127e+00  2.46153846e+00  8.10810811e+00
  6.38888889e+00  7.83289817e-01 -1.01036269e+01  5.47550432e+00
  7.92349727e+00  2.78481013e+00 -1.92307692e+00  1.04575163e+01
 -8.87573964e+00 -1.03896104e+01  1.59420290e+01  1.18750000e+01
 -1.50837989e+01 -3.28947368e+00 -3.40136054e+00  0.00000000e+00
 -5.63380282e+00 -1.07784431e+01  8.05369128e+00 -7.45341615e+00
 -4.02684564e+00 -6.99300699e+00  1.12781955e+01 -6.08108108e+00
 -1.07913669e+01 -8.06451613e-01  1.21951220e+01  5.07246377e+00
 -1.45454545e+01 -1.91489362e+01 -5.26315789e+00 -2.77777778e+00
 -4.28571429e+00 -2.98507463e+00  2.00000000e+01 -1.28205128e+00
 -1.29870130e+01  1.49253731e+01  0.00000000e+00 -7.60869565e+00
 -6.47058824e+00  9.43396226e+00  5.74712644e+00 -1.19565217e+01
 -1.23456790e+00 -5.62500000e+00  4.63576159e+00 -1.13924051e+01
  8.57142857e+00  9.86842105e+00  9.43089431e+00 -2.82317979e+00
 -1.52905199e+00  1.24223602e+00  7.36196319e+00 -1.71428571e+00
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  1.85979971e+00 -1.26984127e+01  1.81818182e+01  6.15384615e+00
 -1.01449275e+01  1.61290323e+00  1.26984127e+01 -1.26760563e+01
 -3.22580645e+00  5.00000000e+00  4.44444444e+01 -5.49450549e+00
  8.10810811e+00 -5.00000000e+00  2.63157895e+01  9.89583333e+00
 -9.95260664e+00 -1.31578947e+01  1.21212121e+00  8.98203593e+00
  1.97802198e+01  5.50458716e+00 -8.69565217e+00 -1.63934426e+01
  1.37254902e+01  8.62068966e+00  1.11111111e+01  0.00000000e+00
  0.00000000e+00 -5.71428571e+00  3.03030303e+00  7.35294118e+00
  1.64383562e+01 -2.35294118e+00 -1.30434783e+01 -3.25000000e+01
  2.59259259e+01  8.82352941e+00  2.70270270e+01  2.12765957e+00
  4.16666667e+00  8.00000000e+00 -1.85185185e+00  1.69811321e+01
  1.12903226e+01 -2.27272727e+00 -5.42635659e+00 -1.63934426e+00
 -7.50000000e+00  1.80180180e+01  9.92366412e+00 -6.25000000e+00
  5.18518519e+00 -3.52112676e+00 -2.91970803e+00  1.87969925e+01
  1.69491525e+01  1.15942029e+01  4.54545455e+00  2.48447205e+00
  1.63636364e+01  9.89583333e+00  1.37440758e+01  7.08333333e+00
 -1.43968872e+01  4.90909091e+01 -1.82926829e+00 -3.33333333e+01
 -5.00000000e+01  3.33333333e+01  5.00000000e+01  0.00000000e+00
  1.66666667e+01  1.00000000e+02  1.00000000e+02  0.00000000e+00
  3.92857143e+01  2.56410256e+01  2.22222222e+01 -9.09090909e+00
 -4.00000000e+01  1.33333333e+02  1.42857143e+01  4.37500000e+01
  4.78260870e+01 -2.05882353e+01  1.11111111e+01  1.66666667e+01
 -8.57142857e+00 -3.07692308e+01  1.11111111e+01  3.00000000e+01
  2.69230769e+01  1.51515152e+01  1.31578947e+01  2.09302326e+01
 -9.61538462e-01 -9.70873786e-01  4.50980392e+01 -1.35135135e+01
  3.33333333e+00 -2.25806452e+01  6.66666667e+01  1.75000000e+01
  1.27659574e+01  1.13207547e+01  2.54237288e+01  5.40540541e+00
  2.56410256e+00  1.87500000e+01  3.15789474e+00 -1.03825137e+01
  6.09756098e-01  6.06060606e+00  1.65714286e+01  3.92156863e+00
 -1.27358491e+01  8.64864865e+00  2.48756219e+01  4.78087649e+00
 -7.98479087e+00  1.11570248e+01  4.44444444e+00  8.51063830e+00
 -9.80392157e+00  1.30434783e+01  1.92307692e+00  1.13207547e+01
  1.52542373e+01 -8.82352941e+00  6.45161290e+00  2.42424242e+01
 -2.07317073e+01  4.28571429e+01 -1.50000000e+01  1.17647059e+01
 -5.26315789e+00  1.66666667e+01 -4.76190476e+00  0.00000000e+00
  1.00000000e+01 -4.54545455e+00  1.42857143e+01  2.08333333e+01
  3.00000000e+02 -6.66666667e+01  1.50000000e+02  0.00000000e+00
 -4.00000000e+01  1.66666667e+02  3.75000000e+01 -5.45454545e+01
  8.00000000e+01  1.11111111e+01 -8.62068966e+00 -1.88679245e+00
  7.69230769e+00  1.96428571e+01  1.64179104e+01 -1.02564103e+01
  1.14285714e+01  1.41025641e+01  0.00000000e+00  4.04494382e+01
  1.36000000e+01  1.25000000e+01 -2.88888889e+01 -1.25000000e+01
  8.21428571e+01  1.17647059e+01 -1.75438596e+01 -1.91489362e+01
  2.63157895e+01  1.87500000e+01 -7.01754386e+00  0.00000000e+00
 -1.81818182e+01 -3.33333333e+01 -1.66666667e+01  0.00000000e+00
  1.20000000e+02  9.09090909e+00  2.50000000e+01 -4.66666667e+01
  6.25000000e+01  6.92307692e+01  9.09090909e+00 -1.00000000e+02
 -1.00000000e+02  0.00000000e+00  6.00000000e+02 -2.85714286e+01
  9.09090909e+00 -8.33333333e+00 -9.09090909e+00  0.00000000e+00
  7.00000000e+01  5.29411765e+01 -1.15384615e+01 -3.91304348e+01
  6.42857143e+01  1.30434783e+01  3.84615385e+00  2.05882353e+01
  9.75609756e+00  1.11111111e+01  5.00000000e+01  6.66666667e+00
  5.00000000e+00  3.57142857e+00  2.75862069e+01 -9.00900901e-01
  1.45454545e+01 -2.38095238e+00  5.55555556e+01 -4.28571429e+01
  1.00000000e+02  2.50000000e+01  0.00000000e+00 -5.00000000e+00
 -5.26315789e+00  1.11111111e+01  2.00000000e+01  8.33333333e+00
 -3.07692308e+01  0.00000000e+00  6.66666667e+01 -2.00000000e+01
 -7.50000000e+01  0.00000000e+00  2.50000000e+01  2.17391304e+01
  7.14285714e-01  1.77304965e+01 -7.83132530e+00 -1.04575163e+01
  1.60583942e+01  1.06918239e+01 -4.54545455e+00  8.33333333e+00
  7.14285714e+00  5.00000000e+01 -1.00000000e+02 -1.00000000e+02
 -3.33333333e+01  0.00000000e+00 -2.17391304e+00 -1.77777778e+01
 -2.70270270e+00  4.44444444e+01  2.30769231e+01 -1.71875000e+01
  1.88679245e+01  0.00000000e+00 -1.11111111e+01  5.00000000e+01
  1.19047619e+01 -3.33333333e+01  2.50000000e+01 -2.00000000e+01
 -5.00000000e+01  4.50000000e+02  9.09090909e+00  5.00000000e+01
 -5.55555556e+00 -5.88235294e+00  2.50000000e+01  4.00000000e+01
  1.33333333e+01  0.00000000e+00  1.17647059e+01 -1.57894737e+01
 -6.25000000e+00  8.00000000e+01 -2.59259259e+01  1.50000000e+01
  0.00000000e+00  8.69565217e+00  2.80000000e+01  7.14285714e+00
 -1.33333333e+00  5.40540541e+00  1.41025641e+01  1.57303371e+01
 -2.13592233e+01  1.97530864e+01  2.06185567e+00  1.31313131e+01
  3.03571429e+01 -4.10958904e+00  1.14636097e+00  3.40010543e+00
  2.52357889e+00  2.95872700e+00  1.61796667e+00  1.24762357e+00
  1.61952822e+00  1.54752281e+00  2.46787217e+00  9.24528302e+00
  3.07832978e+00]
Predicted: [ 8.06933817  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199
 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123
  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581
  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199
 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123
  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581
  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199
 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123
  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581
  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199
 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123
  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581
  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199
 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  9.97654199 10.35798275 10.73942352 11.12086428 11.88374581
  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199
 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123
  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581
  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199
 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.73942352 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123
  9.97654199 10.35798275 10.73942352 11.50230505 11.88374581  8.06933817
  8.45077893  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275
 10.73942352 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893
  8.8322197   9.21366046  9.59510123  9.97654199 10.35798275 10.73942352
 11.12086428 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197
  9.21366046  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428
 11.50230505 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046
  9.59510123  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505
 11.88374581  8.06933817  8.45077893  8.8322197   9.21366046  9.59510123
  9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581
  8.06933817]


Age Group: 11-12
Boys Growth:
Actual: [-1.80180180e+01  1.09890110e+01  4.95049505e+00  0.00000000e+00
 -1.79245283e+01  3.44827586e+00 -1.11111111e+00  3.37078652e+00
  1.08695652e+00  1.07526882e+01  3.88349515e+00  0.00000000e+00
 -1.15807759e-01 -4.34782609e+00  1.75757576e+00 -1.60810006e+00
 -1.21065375e+00 -5.20833333e+00 -1.03425986e+00 -3.46178968e+00
  6.08930988e-01 -2.35373235e+00 -5.24344569e+00  1.14185332e+00
  1.73686496e+00  9.38967136e-01 -5.28541226e+00 -3.30357143e+00
  9.69529086e-01 -5.98994056e+00 -6.66342412e+00 -5.21104742e-02
 -3.75391032e+00  6.81051922e+00  1.22159091e+01  6.89170183e+00
  1.31578947e+00  7.79220779e-01 -3.09278351e+00 -3.16489362e+00
 -3.29579786e+00 -1.04515763e+01 -4.31335236e+00 -9.77792509e+00
 -1.72910663e+00 -3.51906158e+00  1.18541033e+01  0.00000000e+00
  5.16304348e+00  4.39276486e+00 -4.45544554e+00  1.03626943e+00
  2.56410256e-01  7.16112532e+00 -3.57995227e+00 -9.52380952e+00
 -6.57894737e+00  3.52112676e+00 -2.04081633e+00 -2.08333333e+00
 -7.09219858e+00 -5.34351145e+00  3.22580645e+00 -1.56250000e+00
  1.74603175e+01 -6.75675676e-01  1.54929577e+00  6.51872399e+00
  6.51041667e-01  5.95084088e+00  2.68620269e+00  7.25326992e+00
  3.32594235e-01  8.95027624e+00  5.67951318e+00  6.62188100e+00
  2.70027003e-01  2.09424084e+00  2.05128205e+00  6.03015075e+00
  1.32701422e+01  4.18410042e+00  1.60642570e+00  0.00000000e+00
  2.76679842e+00 -1.00000000e+01  1.32478632e+01 -3.39622642e+00
 -2.39374695e+00 -1.30130130e+00 -2.28194726e+00  3.16554229e+00
  6.58953722e+00 -2.12364323e+00 -2.41080039e-01  4.83325278e-02
 -4.83091787e-01  5.87378641e+00  9.62861073e-01 -7.46015599e-01
  6.52545268e+00  5.94932649e+00 -4.84334796e-01 -4.01520913e+00
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  5.92216582e+00 -1.11821086e+00 -5.16962843e+00 -6.81431005e-01
  4.97427101e+00 -8.49673203e+00  6.07142857e+00 -1.85185185e+00
  0.00000000e+00 -2.57575758e+01  3.87755102e+01  7.35294118e-01
 -1.60583942e+01 -1.47826087e+01  4.59183673e+01 -1.25874126e+01
 -7.20000000e+00  2.50000000e+01  1.37931034e+01 -1.50564617e+00
  1.33757962e+00  2.76555625e+00 -2.01834862e+00  1.62297129e+00
 -1.04422604e+00  2.48292986e+00  2.42277408e+00 -2.95683028e+00
  5.11882998e+00  8.69565217e+00  2.41423126e+00 -5.64516129e+00
  3.02432610e+00  5.42437779e+00 -5.69007264e+00  2.56739409e+00
 -8.13516896e-01 -3.72239748e+00 -1.96592398e-01  1.18187787e+00
 -2.92018170e+00  1.36986301e+01  2.16867470e+01  2.97029703e+00
 -1.63461538e+01  2.29885057e+01  1.68224299e+01 -1.52000000e+01
 -1.13207547e+01 -4.25531915e+00  3.33333333e+00  1.18279570e+01
 -1.03363413e+00 -6.63129973e-02  8.46051758e-01  1.00345452e+00
 -1.23778502e+00  1.36873351e+00  1.95217179e-01 -1.47751258e+00
  3.46077785e-01 -1.64230580e-01 -2.13850962e-01 -4.16922134e+00
 -5.11836212e-01  2.05787781e+00  4.03276623e+00 -2.18049667e+00
 -8.66873065e-01 -5.37164272e+00 -3.89438944e+00 -6.04395604e+00
  2.19298246e-01 -2.62582057e+00 -8.99742931e-01 -9.98702983e+00
 -7.20461095e-01  3.62844702e+00 -4.20168067e+00 -4.23976608e+00
 -5.64885496e+00 -9.54692557e+00 -9.83899821e+00  7.14285714e+00
  0.00000000e+00 -5.20231214e+00 -6.46341463e+00  4.82398957e+00
 -3.98009950e+00  1.29533679e+00 -3.58056266e+00 -7.95755968e-01
 -1.20320856e+00 -1.13667118e+01  2.44274809e+00 -6.85543964e+00
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  6.21621622e+00  1.52671756e+00 -2.33352305e+00  1.94250194e-02
  2.71897456e-01  2.16928143e+00 -2.46445498e-01 -7.06955530e+00
 -1.49284254e+00 -5.60514843e-01 -1.11691023e+01  7.35605170e+00
  1.02889667e+00  5.33980583e+00 -8.75576037e+00  8.08080808e+00
  9.34579439e+00  1.45299145e+01 -3.35820896e+00 -2.31660232e+00
  1.26482213e+01  5.61403509e+00  1.96013289e+01 -1.38888889e+00
 -4.62519936e+00  1.05351171e+01  7.71558245e+00  3.23033708e+00
  6.53061224e+00  2.55427842e-01  1.65605096e+00  5.13784461e+00
 -1.43027414e+00  4.83675937e+00  1.96078431e+00  8.66425993e+00
  3.32225914e-01 -4.63576159e+00  4.16666667e+00  3.33333333e+00
  7.74193548e+00  2.99401198e+00 -1.16279070e+00 -4.11764706e+00
 -2.45398773e+00  3.14465409e+00  1.46341463e+01 -4.78723404e+00
  1.56424581e+01 -6.76328502e+00 -6.21761658e+00  2.09944751e+01
 -9.13242009e-01  9.67741935e+00 -6.30252101e+00 -2.24215247e+00
  5.50458716e+00  9.80392157e-01  2.91262136e+00  3.77358491e-01
  5.26315789e+00 -5.35714286e-01 -7.18132855e-01 -1.80831826e+00
 -2.39410681e+00 -3.58490566e+00  4.69667319e+00  2.80373832e+00
  2.89761751e+00  5.00625782e-01  9.09090909e+00  3.02511416e+00
  6.42659280e+00  1.26496616e+01  6.60813309e+00 -2.16731686e+00
 -3.91670359e+01  4.03495994e+01  6.48676700e+00  1.10000000e+03
  2.50000000e+01 -1.33333333e+01 -1.00000000e+02  4.00000000e+02
  1.97183099e+01  2.35294118e+00  1.26436782e+01 -1.83673469e+01
  1.50000000e+01  1.73913043e+01  7.40740741e+01  5.58510638e+01
  2.04778157e+01  4.47592068e+01 -1.76125245e+00  1.84466019e+01
  1.06557377e+01  2.14814815e+01  5.48780488e+00 -3.46820809e+00
  3.59281437e+00 -8.67052023e+00  1.39240506e+01 -1.55555556e+01
  0.00000000e+00  1.38157895e+01  5.11363636e+00 -2.16216216e+00
 -3.68324125e-01  3.32717190e+00  1.78890877e-01  2.85714286e+00
  9.02777778e+00  1.36942675e+01 -1.66666667e+01  2.15126050e+01
  9.68188105e-01  1.49295775e+01 -3.18627451e+00  2.43037975e+01
  1.28309572e+01  4.33212996e+00  3.63321799e+00  6.51085142e+00
 -2.50783699e+00 -1.39871383e+01  1.92523364e+01  3.60501567e+00
  1.30970724e+00  1.52091255e-01  3.41685649e+00  6.09397944e+00
 -6.78200692e+00  2.52412769e+00  7.67559739e+00 -2.55548083e+00
 -3.03657695e+00  7.68683274e+00  9.91407799e-01  1.50837989e+01
  0.00000000e+00  9.70873786e-01  1.82692308e+01 -3.65853659e+00
 -4.21940928e-01  1.77966102e+01  7.19424460e-01 -1.07142857e+00
  5.77617329e+00  1.36518771e+00  0.00000000e+00 -1.41732283e+01
 -1.00917431e+01  1.22448980e+01  1.54545455e+01 -1.33858268e+01
 -9.09090909e-01 -3.66972477e+00 -3.33333333e+01  3.85714286e+01
  7.21649485e+00  0.00000000e+00 -3.40206186e+01  4.21875000e+01
  0.00000000e+00  4.39560440e+00  4.21052632e+00  1.01010101e+01
  9.17431193e+00 -1.68067227e+01  1.91919192e+01  2.03389831e+01
 -3.42163355e+00 -3.42857143e+00  3.66863905e+00 -1.48401826e+00
  1.07763615e+01  4.39330544e+00  1.40280561e+00  3.16205534e+00
 -1.81992337e+00  5.17073171e+00  1.20593692e+00  9.12162162e+00
 -8.66873065e+00  8.13559322e+00 -2.19435737e+00 -5.12820513e+00
  6.41891892e+00  6.66666667e+00 -1.13095238e+01 -7.04697987e+00
  1.69675090e+01  1.11111111e+01 -2.71604938e+01 -2.54237288e+00
  2.60869565e+01  2.06896552e+00 -6.75675676e+00  9.42028986e+00
  3.97350993e+00  3.18471338e+00 -8.64197531e+00  1.41891892e+01
  1.00591716e+01  8.57142857e+00 -1.05263158e+01 -2.35294118e+01
  1.53846154e+01 -2.33333333e+01  4.78260870e+01  1.47058824e+01
 -4.35897436e+01  1.81818182e+01  1.23076923e+02  0.00000000e+00
  1.81818182e+01  1.15384615e+01 -5.74712644e+00 -9.75609756e+00
  0.00000000e+00  1.35135135e+01  4.76190476e+00 -7.95454545e+00
  4.93827160e+00  1.64705882e+01 -1.81818182e+01 -2.87026406e+00
 -1.12293144e+01  7.98934754e-01  1.20211361e+01  6.48584906e+00
  8.41638981e+00  3.88151175e+00  3.63815143e+00  4.45920304e+00
  1.80744777e+01  3.53846154e+00 -8.61538462e+00 -1.68350168e+00
 -2.05479452e+00 -1.74825175e+00 -1.06761566e+00 -6.47482014e+00
  3.84615385e+00 -7.77777778e+00 -1.44578313e+01  8.92018779e+00
  1.29310345e+00 -7.69230769e+00  1.66666667e+01 -2.50000000e+01
  2.85714286e+01  7.40740741e+00  4.48275862e+01 -3.09523810e+01
 -1.72413793e+01  1.25000000e+01  0.00000000e+00  1.11111111e+01
 -9.90615224e+00 -4.62962963e-01  5.93023256e+00 -4.93962678e+00
  5.77367206e-01 -3.32950631e+00 -3.20665083e+00  1.04294479e+01
 -1.24444444e+01 -2.03045685e+00  1.02331606e+01 -4.54545455e+01
  0.00000000e+00 -4.44444444e+01  5.00000000e+01  3.33333333e+01
  2.50000000e+01 -1.20000000e+01  1.81818182e+01 -1.92307692e+01
  9.52380952e+00 -8.69565217e+00 -1.37931034e+00 -6.64335664e+00
  1.17977528e+01  1.67504188e-01 -8.19397993e+00  2.73224044e+00
 -1.77304965e-01  1.01243339e+01 -7.74193548e+00  1.36363636e+01
  2.06153846e+01 -1.13821138e+01  4.58715596e+00  2.10526316e+01
 -1.52173913e+01  1.62393162e+01  5.14705882e+00 -2.79720280e+00
  1.07913669e+01  7.14285714e+00 -7.87878788e+00  7.89473684e+00
 -2.02702703e+00 -6.20689655e+00 -3.30882353e+00  1.06463878e+01
  1.23711340e+01  7.33944954e+00  6.83760684e+00  1.17333333e+01
 -1.40811456e+01  2.22222222e+00 -2.71739130e-01  6.51629073e+00]
Predicted: [1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043
 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043
 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043
 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043
 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 4.49145442 5.04720454 5.60295466 6.15870477 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043
 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043
 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406
 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477
 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418
 3.9357043  4.49145442 5.04720454 5.60295466 6.15870477 6.71445489
 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043
 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501
 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442
 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382
 2.26845394 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454
 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394
 2.82420406 3.37995418 3.9357043  4.49145442 5.04720454 5.60295466
 6.15870477 6.71445489 7.27020501 1.71270382]

Girls Growth:
Actual: [ 2.50000000e+01 -4.00000000e+01  3.33333333e+01  7.50000000e+01
  4.28571429e+01  1.00000000e+01  0.00000000e+00  0.00000000e+00
  3.63636364e+01  2.66666667e+01  5.26315789e+00  6.17977528e+00
 -4.23280423e+00 -9.94475138e+00  2.39263804e+01 -9.40594059e+00
  1.47540984e+01 -2.85714286e+00 -5.88235294e+00  2.60416667e+00
  7.61421320e+00  8.96226415e+00  8.29015544e+00 -4.30622010e+00
  3.00000000e+00  3.39805825e+00  1.92488263e+01  1.96850394e+00
 -1.54440154e+00  8.62745098e+00 -7.94223827e+00  1.64705882e+01
  0.00000000e+00 -5.37313433e+00  1.29337539e+01  2.03910615e+01
  1.16009281e+00  3.89908257e+00 -8.38852097e+00  3.13253012e+00
 -3.50467290e+00 -8.23244552e+00  1.26649077e+01 -1.40515222e+00
  1.11111111e+01  1.33333333e+01  5.88235294e+00  0.00000000e+00
  1.66666667e+01 -2.38095238e+00  7.31707317e+00  4.54545455e+00
  4.56521739e+01  7.46268657e+00 -2.50000000e+01 -4.61538462e+01
  8.57142857e+01 -7.69230769e+00  0.00000000e+00  8.33333333e+00
  3.84615385e+01 -5.55555556e+00  2.35294118e+01 -2.38095238e+01
  1.25000000e+01  1.66666667e+01 -1.17647059e+01  2.88888889e+01
  2.58620690e+01 -1.23287671e+01  2.65625000e+01  1.35802469e+01
  2.17391304e+01  6.25000000e+00  3.36134454e+00  2.84552846e+01
  1.26582278e+01  0.00000000e+00  0.00000000e+00  4.11764706e+01
  1.25000000e+01  1.85185185e+01  6.25000000e+00 -2.05882353e+01
  3.70370370e+01 -2.16216216e+01  2.06896552e+01  2.57142857e+01
  6.22406639e-01 -7.83505155e+00  2.23713647e+00 -2.62582057e+00
  2.24719101e-01  2.91479821e+00  1.43790850e+01  7.23809524e+00
  6.03907638e+00  8.04020101e+00  6.35658915e+00  7.36240172e+00
 -6.65778961e-02  3.79746835e+00  1.09114249e+00 -1.46031746e+00
 -1.80412371e+00  4.72440945e+00 -4.26065163e+00  9.16230366e-01
  9.01426719e+00 -4.40214158e+00 -3.90625000e+00 -1.01626016e+01
  1.13122172e+00  3.80313199e+00  7.11206897e+00 -3.82293763e+00
 -6.27615063e-01  1.05263158e+00  8.33333333e-01  2.04545455e+01
  4.63121784e+00  1.95121951e+01 -8.16326531e+00  2.00000000e+01
  3.88888889e+01 -1.86666667e+01  1.47540984e+01  3.00000000e+01
 -6.59340659e+00 -1.29411765e+01  1.08108108e+01  1.09756098e+01
 -3.33333333e+01  6.00000000e+01 -3.75000000e+01  1.00000000e+01
 -9.09090909e+00  6.00000000e+01 -6.25000000e+00 -3.33333333e+01
  0.00000000e+00  9.00000000e+01  4.21052632e+01  8.33333333e+00
 -1.53846154e+00  1.01562500e+01  1.41843972e+00  9.79020979e+00
 -5.09554140e+00  5.36912752e+00  1.40127389e+01 -5.58659218e-01
  2.69662921e+01 -1.32743363e+00 -5.47945205e+00  1.52173913e+01
 -8.80503145e+00  4.82758621e+00 -7.89473684e+00  2.85714286e+00
  1.66666667e+01  4.76190476e+00  5.68181818e-01  2.31638418e+01
 -2.29357798e+00  1.53846154e+01 -6.66666667e+00 -1.42857143e+01
 -1.66666667e+01  5.00000000e+01  4.66666667e+01 -1.36363636e+01
 -3.15789474e+01  3.84615385e+01  0.00000000e+00  2.77777778e+01
 -5.93803787e+00 -2.05855444e+00 -1.40121439e-01 -1.54349860e+00
  1.47268409e+00  2.01310861e+00  2.61587884e+00  1.47584973e+00
 -7.05156457e-01  5.41500222e+00  3.07368421e+00  8.13008130e-01
  3.49462366e+00  4.15584416e+00  5.23690773e+00 -2.84360190e+00
 -6.58536585e+00  7.83289817e+00  6.77966102e+00 -9.07029478e+00
  0.00000000e+00 -9.97506234e-01  5.61797753e-01 -7.82122905e+00
 -6.06060606e-01  7.31707317e+00  7.38636364e+00 -1.21693122e+01
  3.01204819e+00 -2.33918129e+00 -2.15568862e+01  6.10687023e+00
  8.63309353e+00 -1.79347826e+01 -1.12582781e+01 -2.98507463e+00
  2.23076923e+01  2.51572327e+00 -1.53374233e+01 -4.34782609e+00
  5.30303030e+00  1.36690647e+01  5.06329114e+00 -1.80722892e+00
  0.00000000e+00 -4.34782609e+00 -9.09090909e+00  8.75000000e+00
  4.59770115e+00 -6.59340659e+00  3.52941176e+00 -7.95454545e+00
 -1.60493827e+01  3.52941176e+01 -4.34782609e+00  6.43274854e+00
  4.94505495e+00 -5.23560209e+00 -2.76243094e+00  6.81818182e+00
  3.72340426e+00 -5.64102564e+00  3.26086957e+00 -7.36842105e+00
 -1.76136364e+01  1.31034483e+01  5.29100529e-01 -5.52631579e+00
  5.01392758e+00  1.98938992e+00 -4.68140442e+00  1.36425648e-01
 -1.36239782e-01  4.50204638e+00 -3.91644909e+00  7.88043478e+00
  8.81612091e-01  1.36986301e+01 -4.09638554e+01  1.63265306e+01
  2.45614035e+01  5.63380282e+00  0.00000000e+00 -1.33333333e+00
  1.75675676e+01 -1.14942529e+00  1.74418605e+01  1.48514851e+01
  1.33333333e+01  7.35294118e+00 -1.82648402e+00 -1.76744186e+01
  9.03954802e+00  6.73575130e+00  1.26213592e+01  8.62068966e+00
 -9.92063492e+00  1.18942731e+01  7.48031496e+00  5.35714286e+00
  1.18644068e+01  4.54545455e+00  5.79710145e+00 -1.36986301e+01
 -1.42857143e+01  2.77777778e+01  1.88405797e+01 -1.09756098e+01
  1.36986301e+01  8.43373494e+00  1.11111111e+01  1.66666667e+01
  8.57142857e+00 -5.26315789e+00  3.05555556e+01 -6.38297872e+00
  2.04545455e+01  9.43396226e+00  3.44827586e+00  1.33333333e+01
 -1.47058824e+01 -3.52112676e+00 -5.10948905e+00  6.92307692e+00
  5.75539568e+00 -4.08163265e+00  1.27659574e+01 -8.80503145e+00
 -4.82758621e+00  1.44927536e+01  2.34177215e+01 -5.64102564e+00
 -1.02739726e+01 -3.05343511e+00  3.93700787e+01  1.46892655e+01
  2.46305419e+00  2.59615385e+01  2.13740458e+01  1.76100629e+01
 -3.95721925e+01  3.62831858e+01  2.20779221e+01 -2.00000000e+01
  2.50000000e+01  1.20000000e+02 -4.54545455e+01  8.33333333e+01
  9.09090909e+00  7.50000000e+01  8.09523810e+01  1.05263158e+01
  8.33333333e+01 -1.16883117e+01 -1.42857143e+01  5.00000000e+01
  5.55555556e+01  7.14285714e+01  3.75000000e+01  6.06060606e+00
  8.57142857e+00  5.26315789e+00 -4.25000000e+01  3.91304348e+01
 -1.56250000e+01  1.91489362e+01  8.92857143e+00  2.62295082e+01
  1.55844156e+01  6.74157303e+00  1.47368421e+01  1.83486239e+01
  1.16279070e+01 -2.36111111e+01  1.36363636e+01  4.88000000e+01
 -1.03448276e+01  5.76923077e+01  1.95121951e+01  4.08163265e+00
  1.96078431e+00  3.46153846e+01  2.42857143e+01  1.03448276e+01
 -8.33333333e+00  1.13636364e+01  1.12244898e+01  4.62427746e+00
  8.28729282e+00  5.61224490e+00 -5.31400966e+00  2.55102041e+00
  2.63681592e+01  6.69291339e+00 -5.53505535e+00 -7.81250000e+00
  2.75423729e+01  1.76079734e+01 -2.74509804e+01 -2.70270270e+00
  5.83333333e+01  3.50877193e+00  1.18644068e+01  3.03030303e+00
 -2.94117647e+00  1.06060606e+01 -1.09589041e+01  1.53846154e+00
  1.66666667e+01  1.17647059e+01  5.26315789e+00  5.00000000e+00
  3.33333333e+01 -3.57142857e+00  3.70370370e+00  1.07142857e+01
  3.22580645e+00 -2.81250000e+01  3.91304348e+01  1.25000000e+01
 -7.00000000e+01  1.50000000e+02  2.00000000e+01  3.33333333e+01
  6.25000000e+01  3.84615385e+01 -3.88888889e+01  0.00000000e+00
  5.45454545e+01 -5.88235294e+00 -2.57575758e+01  2.85714286e+01
  1.74603175e+01  2.02702703e+01 -8.98876404e+00  1.23456790e+00
  1.95121951e+01  2.34693878e+01 -1.65289256e+00  3.19327731e+01
  1.14649682e+01 -1.17647059e+01  2.33333333e+01  1.08108108e+01
  0.00000000e+00 -2.43902439e+01  2.90322581e+01  1.25000000e+01
 -2.22222222e+01  2.00000000e+01  2.61904762e+01  1.13207547e+01
 -1.25000000e+01 -5.71428571e+01  2.00000000e+02  3.33333333e+01
  3.33333333e+01 -1.25000000e+01  5.00000000e+01  1.90476190e+01
 -3.20000000e+01  4.70588235e+01  1.60000000e+01 -3.33333333e+01
  0.00000000e+00  0.00000000e+00  1.66666667e+02 -1.25000000e+01
 -2.85714286e+01  4.00000000e+01 -3.33333333e+01  8.33333333e+00
  5.38461538e+01  4.50000000e+01  3.44827586e+00 -3.33333333e+01
  3.00000000e+01  2.69230769e+01 -1.81818182e+01  3.70370370e+00
  1.42857143e+01  2.24489796e+01 -1.66666667e+00  8.47457627e+00
  2.65625000e+01  7.40740741e+00 -1.14942529e+01  1.68831169e+01
  2.00000000e+01  7.40740741e+00  2.75862069e+01  2.02702703e+01
  1.00000000e+01  0.00000000e+00 -3.63636364e+01  4.28571429e+01
 -2.50000000e+01  4.00000000e+01  0.00000000e+00 -9.52380952e+00
  5.26315789e+00  2.00000000e+01  8.33333333e+00  1.33333333e+02
 -4.28571429e+01  2.50000000e+01  4.00000000e+01 -4.28571429e+01
  5.78512397e+00  7.03125000e+00  1.53284672e+01 -1.96202532e+01
  5.51181102e+00  1.04477612e+01  9.45945946e+00  8.64197531e+00
 -5.11363636e+00  1.19760479e+00  5.91715976e-01  0.00000000e+00
  0.00000000e+00  0.00000000e+00  3.00000000e+02  3.33333333e+01
 -7.50000000e+01 -4.25531915e+00  2.22222222e+00  1.08695652e+01
  1.37254902e+01 -5.17241379e+00  1.81818182e+01  2.15384615e+01
  1.77215190e+01 -1.93548387e+01  2.26666667e+01  2.82608696e+01
  2.00000000e+02  6.66666667e+01  8.00000000e+01 -3.33333333e+01
  6.66666667e+01  7.00000000e+01  5.88235294e+00  0.00000000e+00
  5.55555556e+00  3.68421053e+01 -1.53846154e+01 -1.42857143e+01
  5.55555556e+00 -2.10526316e+01 -1.33333333e+01  9.23076923e+01
  1.60000000e+01  1.03448276e+01 -9.37500000e+00 -6.89655172e+00
  4.07407407e+01 -2.63157895e+00 -1.40845070e+00  2.28571429e+01
  1.97674419e+01  9.70873786e-01 -9.61538462e+00  2.44680851e+01
  2.56410256e+00  3.50000000e+01 -2.22222222e+01  9.52380952e+00
  2.89855072e+00 -6.30182421e-01 -6.45304851e-01  4.36730123e+00
  2.21030043e+00  1.40667646e+00  1.95652174e+00  5.43202356e+00
  3.26463790e+00 -4.13130654e+00  1.17120623e+01  3.85754093e+00]
Predicted: [ 6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  6.21696126  6.85823013  7.499499    8.14076786  8.78203673
  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107 12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  6.21696126  6.85823013  7.499499    8.14076786  8.78203673
  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107 12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  6.21696126  6.85823013  7.499499    8.14076786  8.78203673
  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107 12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  6.21696126  6.85823013  7.499499    8.14076786  8.78203673
  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107 12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  9.4233056  10.06457446 10.70584333 11.3471122  12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  6.21696126  6.85823013  7.499499    8.14076786  8.78203673
  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107 12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  6.21696126  6.85823013  7.499499    8.14076786  8.78203673
  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107 12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013  7.499499
  8.14076786  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122
 11.98838107 12.62964993  6.21696126  6.85823013  7.499499    8.14076786
  8.78203673  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107
 12.62964993  6.21696126  6.85823013  7.499499    8.14076786  8.78203673
  9.4233056  10.06457446 10.70584333 11.3471122  11.98838107 12.62964993
  6.21696126  6.85823013  7.499499    8.14076786  8.78203673  9.4233056
 10.06457446 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126
  6.85823013  7.499499    8.14076786  8.78203673  9.4233056  10.06457446
 10.70584333 11.3471122  11.98838107 12.62964993  6.21696126  6.85823013
  7.499499    8.14076786  8.78203673  9.4233056  10.06457446 10.70584333
 11.3471122  11.98838107 12.62964993  6.21696126]


Age Group: 9-10
Boys Growth:
Actual: [-1.44329897e+01 -7.22891566e+00 -6.49350649e+00  9.72222222e+00
 -1.26582278e+00  1.79487179e+01  1.30434783e+01  3.84615385e+00
 -1.20370370e+01 -1.78947368e+01  1.02564103e+01 -8.65176640e-01
  9.67272727e+00 -4.70822281e+00 -4.17536534e+00 -2.83224401e+00
  8.96860987e+00  3.29218107e+00 -2.32403718e+00 -8.97348742e+00
 -2.31516057e+00 -2.06422018e+00 -3.62249762e+00  3.65974283e+00
 -2.00381679e+00 -5.69620253e+00  4.74961280e+00  1.67570232e+00
  3.39311682e+00 -6.28223160e+00 -1.52576288e+01  7.37898465e+00
  3.46344145e+00  6.95047785e-01  1.58182341e+00  6.93657984e+00
  2.03865502e+00  1.34924754e+00 -2.25294419e+00 -4.32163436e+00
 -6.43306871e+00 -1.96898771e+01 -1.31147541e+00 -2.76854928e+00
  1.65745856e+00  2.98913043e+00  8.70712401e+00  1.69902913e+00
 -5.72792363e+00  3.29113924e+00  1.10294118e+01 -9.05077263e+00
 -5.82524272e+00  1.28865979e+00  6.36132316e+00 -3.75527426e+01
  6.75675676e+00 -1.20253165e+01 -7.19424460e+00 -1.55038760e+01
  1.74311927e+01  3.12500000e+00  2.12121212e+01 -1.68750000e+01
 -4.51127820e+00 -7.87401575e+00  7.64331210e-01 -3.91908976e+00
  4.73684211e+00  2.26130653e+00  5.77395577e+00  7.20092915e+00
  7.80065005e+00  1.27638191e+01 -1.19429590e+01  1.93319838e+01
  6.10687023e+00  5.82010582e+00  2.05000000e+01  2.48962656e+00
 -2.42914980e+00  3.73443983e+00  5.60000000e+00 -6.43939394e+00
 -1.61943320e+00 -1.27572016e+01  1.03773585e+01 -4.70085470e+00
 -2.30436870e+00  4.42260442e-01  4.45205479e+00  5.15222482e-01
  1.07176142e+00  8.29875519e-01  2.28623685e+00 -4.47027269e-02
 -3.89087657e+00  5.21172638e+00 -1.32684653e+00  6.68758256e+00
  1.54774803e+00 -2.08809633e+00 -4.04732254e+00 -1.75210902e+00
 -3.64927345e+00 -4.42159383e+00 -4.07028869e+00 -8.99065421e+00
  2.36188129e+00 -4.39406100e+00 -4.03153153e+00 -3.05092701e-01
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 -5.63106796e+00 -3.26278660e+00 -8.59920997e+00  7.04787234e+00
  5.59006211e-01  9.72762646e+00  2.30496454e+00  6.06585789e+00
 -9.96732026e+00  5.44464610e+00  1.89328744e+00 -1.01351351e+00
 -5.80204778e+00 -3.80434783e+00  9.22787194e+00 -2.06896552e+00
  2.32142857e+01  1.30434783e+01 -1.73076923e+01 -5.42635659e+00
 -1.63934426e+00  1.66666667e+01  1.00000000e+01  1.29870130e+00
 -1.28205128e+00  1.94805195e+01 -9.23913043e+00  9.09090909e-01
 -2.44530245e+00  1.25329815e+00  9.12052117e-01  3.03421562e+00
  3.69674185e+00  1.69184290e+00  1.60427807e+00  1.16959064e-01
  1.10397196e+01  6.83850605e-01  1.65892502e+00  5.87467363e+00
 -3.82244143e+00 -2.11538462e+00 -2.35756385e+00  3.28638498e+00
  8.70129870e+00 -1.07526882e+00 -1.25000000e+01  4.07177364e+00
 -2.78514589e+00  4.42477876e+00 -1.69491525e+01  1.93877551e+01
  1.36752137e+01 -1.12781955e+01  4.23728814e+00  2.43902439e+00
 -1.58730159e+01 -2.07547170e+01  3.80952381e+01 -8.62068966e+00
 -6.31313131e-02 -1.12128869e+00 -2.25203642e+00  3.30065359e+00
  6.32711167e-01 -3.14366551e-02  2.23270440e+00 -2.15318364e+00
 -2.21628419e+00  2.71660505e+00 -3.97496088e+00  4.37580438e+00
  2.71270037e+00 -2.34093637e+00  7.99016595e-01 -3.71951220e+00
 -4.36985434e+00 -1.65562914e+00 -4.71380471e+00 -9.61130742e+00
  9.85144644e+00  6.12099644e+00 -5.51523948e+00  9.67741935e+00
 -2.52100840e+00 -2.44252874e+00 -3.82916053e+00 -1.39356815e+01
 -5.33807829e-01  5.72450805e+00 -1.60744501e+01  2.05645161e+01
 -3.51170569e+00  3.14207650e+00  1.32450331e+00  4.05228758e+00
 -4.27135678e+00 -1.44356955e+00 -2.79627164e+00 -1.91780822e+00
 -6.42458101e+00 -1.16417910e+01  5.74324324e+00  1.59744409e-01
 -7.04225352e-01 -4.96453901e+00  0.00000000e+00 -3.54477612e+00
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 -1.72093023e+01 -2.24719101e+00  2.29885057e+00 -7.14285714e+00
  8.41346154e+00  5.09977827e+00 -4.00843882e+00 -6.15384615e+00
 -7.49414520e+00  2.53164557e+00  4.19753086e+00 -1.04265403e+01
  8.20105820e+00  7.33496333e-01 -3.52457411e-01 -1.37551582e-01
  9.64187328e-01 -3.37166244e+00 -7.05929810e-01  1.27970750e+00
 -4.01123145e-02  1.30417335e+00 -1.12695583e+01  1.07142857e+01
  2.41935484e+00 -4.21940928e+00  1.49779736e+01  4.98084291e+00
  6.20437956e+00 -1.37457045e+00  3.48432056e-01  6.94444444e+00
  6.16883117e+00 -5.19877676e+00  2.12903226e+01  3.45744681e+00
  1.00430416e+01  2.60756193e+00  6.60736976e+00  2.38379023e-01
  5.46967895e+00  2.59301015e+00  1.42857143e+00  1.62513543e+00
 -2.13219616e-01  9.61538462e-01 -6.77248677e+00 -5.21172638e+00
  6.18556701e+00  8.73786408e+00  3.86904762e+00  1.00286533e+01
 -5.98958333e+00 -2.49307479e+00 -9.09090909e+00 -2.81250000e+00
  1.09324759e+01 -2.31884058e+00  1.02564103e+00  2.03045685e+00
  2.48756219e+00  4.36893204e+00  1.86046512e+00  1.59817352e+01
 -3.93700787e+00  0.00000000e+00 -4.09836066e+00  1.19658120e+01
 -1.06870229e+01 -3.47826087e+00  2.34234234e+00  1.58450704e+00
  8.66551127e-01  0.00000000e+00 -5.67010309e+00 -1.45719490e+00
  1.66358595e+00 -6.00000000e+00  3.09477756e+00  1.87617261e-01
  2.71460015e+00  8.14285714e+00  9.90752972e+00  1.31610577e+01
  1.24800850e+01  8.30972616e+00  5.40540541e+00  1.40612076e+00
 -5.18760196e+01  7.37288136e+01 -9.75609756e-01  3.33333333e+02
  6.92307692e+01 -1.81818182e+01 -9.44444444e+01  1.90000000e+03
  3.65853659e+00 -1.17647059e+01  2.40000000e+01  7.52688172e+00
  2.00000000e+00  6.86274510e+00  1.38532110e+02  4.65384615e+01
  8.66141732e+00  4.51690821e+01 -2.66222962e+00  4.73118280e+01
 -6.56934307e+00  1.95312500e+01 -5.22875817e+00 -2.06896552e+00
  4.92957746e+00  1.14093960e+01 -3.61445783e+00 -1.75000000e+01
  1.66666667e+01  1.10389610e+01  3.49907919e+00 -5.33807829e-01
  3.75670841e+00  4.82758621e+00  4.11184211e+00  1.09004739e+01
  1.99430199e+00  3.49162011e+00 -1.87584345e+01  4.10299003e+01
  2.70906949e+00  1.70940171e+00  8.96358543e+00  3.18766067e+01
  8.57699805e+00  3.41113106e+00  6.42361111e+00  6.03588907e+00
  5.38461538e+00 -1.45985401e+01  2.51282051e+01 -8.19672131e-01
  4.46428571e+00  1.78710179e+00 -2.97709924e+00  2.36034618e+00
  3.92006149e+00  9.61538462e+00  2.56410256e+00 -2.89473684e+00
 -9.75609756e+00  1.66666667e+01  1.09395109e+01  1.13989637e+01
  8.83720930e+00  0.00000000e+00 -6.41025641e+00  1.32420091e+01
  1.00806452e+01  6.95970696e+00  0.00000000e+00 -8.21917808e+00
  2.16417910e+01  3.98773006e+00 -6.48148148e+00  1.98019802e+00
  1.35922330e+01 -9.40170940e+00 -9.43396226e+00  3.12500000e+00
  6.06060606e+00 -8.57142857e+00 -3.12500000e+01  6.66666667e+01
 -8.18181818e+00 -1.05263158e+01 -3.88235294e+01  9.80769231e+01
 -7.76699029e+00  2.10526316e+00  1.85567010e+01  9.56521739e+00
 -5.55555556e+00 -1.17647059e+01  4.47619048e+01 -1.44736842e+01
 -1.51706700e+00  1.39922978e+01 -2.25225225e-01 -5.98194131e+00
  1.32052821e+01  3.28738070e+00  1.31416838e+01 -6.26134301e+00
 -1.44240077e+01  1.95701357e+01 -1.79754021e+00  5.10204082e+00
 -2.03883495e+01 -1.21951220e+00  1.48148148e+01  1.43369176e+00
 -1.23674912e+01 -8.87096774e+00 -5.30973451e+00  1.44859813e+01
  2.28571429e+01  5.31561462e+00  1.22807018e+01  7.81250000e-01
  1.16279070e+01 -1.38888889e+00  1.47887324e+01  2.51533742e+01
 -3.43137255e+00  1.01522843e+00 -1.15577889e+01  7.38636364e+00
  6.34920635e+00 -1.38888889e+01 -1.93548387e+01  2.40000000e+01
 -2.25806452e+01  5.00000000e+01 -1.66666667e+01  2.66666667e+01
  3.68421053e+01 -1.92307692e+00  1.76470588e+01  8.33333333e+00
 -2.66666667e+00  6.84931507e+00 -1.28205128e+00  1.68831169e+01
 -1.00000000e+01  8.64197531e+00  4.09090909e+01  0.00000000e+00
 -4.83870968e+01  3.90625000e+01  2.24719101e+00 -3.25520833e+00
 -3.09555855e+00  5.69444444e+00  7.88436268e+00  7.55176614e+00
  1.29105323e+01  8.62587763e+00  1.44967682e+01 -8.87096774e+00
  2.45132743e+01  7.81805259e-01 -1.49659864e+01  5.20000000e+00
 -5.70342205e+00 -2.41935484e+00  2.06611570e+00  1.21457490e+00
 -7.60000000e+00  5.19480519e+00 -2.30452675e+01  2.19251337e+01
 -3.50877193e+00 -3.75000000e+01  8.00000000e+00 -7.40740741e+00
  3.60000000e+01  1.17647059e+01  4.47368421e+01 -3.27272727e+01
 -8.10810811e+00 -3.52941176e+01  2.72727273e+01 -1.42857143e+01
 -3.14606742e+00 -5.91647332e+00 -7.39827374e-01 -3.47826087e+00
 -1.00386100e+01  2.26037196e+01  7.00116686e+00 -6.21592148e+00
 -1.87209302e+01  1.85979971e+01 -3.25693607e+00 -5.89743590e+01
  2.50000000e+01 -2.00000000e+01  3.12500000e+01  5.23809524e+01
 -6.25000000e+00  1.33333333e+01 -1.47058824e+01 -4.13793103e+01
  1.76470588e+01 -1.00000000e+01 -1.53256705e+00  3.30739300e+00
 -2.63653484e+00  7.73694391e-01  1.34357006e+00  1.89393939e+00
  2.02602230e+01  1.53013910e+01 -2.49329759e+01  4.00000000e+01
  6.37755102e+00 -1.17117117e+01  2.65306122e+01  1.37096774e+01
 -1.27659574e+01  9.75609756e+00  1.48148148e+01  1.41935484e+01
 -2.03389831e+01 -7.80141844e+00  2.61538462e+01  1.40243902e+01
  3.04182510e+00 -2.58302583e+00  5.68181818e+00  8.96057348e+00
  1.51315789e+01  4.37142857e+01 -4.37375746e+00 -9.56340956e+00
 -9.65517241e+00  1.27226463e+01 -1.35440181e+01 -1.44356955e+00]
Predicted: [-2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197
  6.21586284  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197
  6.21586284  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197
  6.21586284  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197
  6.21586284  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851  6.21586284  8.00400597  9.79214911 11.58029224 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197
  6.21586284  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197
  6.21586284  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343
  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224
 13.36843538 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657
  4.4277197   6.21586284  8.00400597  9.79214911 11.58029224 13.36843538
 15.15657851 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197
  6.21586284  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851
 -2.72485283 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284
  8.00400597  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283
 -0.9367097   0.85143343  2.63957657  4.4277197   6.21586284  8.00400597
  9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097
  0.85143343  2.63957657  4.4277197   6.21586284  8.00400597  9.79214911
 11.58029224 13.36843538 15.15657851 -2.72485283]

Girls Growth:
Actual: [-3.33333333e+01  1.50000000e+02  8.00000000e+01 -3.33333333e+01
  5.00000000e+01  4.44444444e+01  3.07692308e+01  2.94117647e+01
 -2.27272727e+01 -5.88235294e+00  1.87500000e+01 -7.81250000e+00
  8.47457627e+00 -3.12500000e+00  5.80645161e+01 -1.58163265e+01
  7.27272727e+00  9.60451977e+00  4.63917526e+00 -3.94088670e+00
  1.02564103e+00  1.06598985e+01 -9.47368421e+00  4.06976744e+00
  2.29050279e+01  0.00000000e+00  4.09090909e+00  9.17030568e+00
  2.04000000e+01  5.31561462e+00 -1.73501577e+01  2.74809160e+01
  1.49700599e+00  6.30136986e+00 -5.15463918e-01  6.99481865e+00
  2.42130751e+00  4.96453901e+00 -6.75675676e-01  2.04081633e+00
  3.77777778e+00 -1.22055675e+01  5.36585366e+00  1.15740741e+00
  1.56250000e+01 -3.24324324e+01  1.20000000e+01 -3.57142857e+00
  4.44444444e+01  3.84615385e+01  2.59259259e+01  1.17647059e+01
 -7.89473684e+00  2.85714286e+00  2.77777778e+00 -3.84615385e+01
  0.00000000e+00  3.75000000e+01  9.09090909e+01 -3.80952381e+01
 -7.69230769e+00  7.50000000e+01 -2.85714286e+01 -1.33333333e+01
  7.69230769e+00  3.57142857e+01  1.61290323e+01 -2.91666667e+01
  5.09803922e+01  4.93506494e+01  8.69565217e+00 -1.12000000e+01
 -9.00900901e-01  3.63636364e+01 -6.66666667e-01  3.35570470e+01
  1.30653266e+01  2.14285714e+01  2.94117647e+01  2.72727273e+01
  1.07142857e+01  0.00000000e+00 -6.45161290e+00 -1.37931034e+01
  1.20000000e+01  2.50000000e+01 -5.71428571e+00  1.21212121e+01
 -2.54777070e+00 -2.17864924e+00 -6.01336303e+00  4.73933649e+00
  1.04072398e+01  1.43442623e+01  1.29032258e+01  4.76190476e-01
  3.31753555e+00  1.66666667e+01 -1.31061599e-01 -1.56356220e+00
  2.48618785e+00  3.30188679e+00 -2.21787345e+00  3.40226818e+00
  2.00000000e+00  4.87033523e+00 -1.20627262e-01 -1.13526570e+01
  1.07629428e+01  2.52152522e+00 -3.40425532e+00  2.42290749e+00
  2.36559140e+00  3.57142857e+00 -1.82555781e+00 -1.03305785e+00
  8.76826722e+00  1.53550864e+01 -8.31946755e+00  1.56079855e+01
 -2.98273155e+00  1.70731707e+01  0.00000000e+00  3.75000000e+01
  6.06060606e+00 -8.57142857e+00  0.00000000e+00  1.71875000e+01
 -5.33333333e+00  1.83098592e+01  7.14285714e+00  0.00000000e+00
 -2.50000000e+01 -8.33333333e+00  9.09090909e+00  0.00000000e+00
  8.33333333e+00 -1.53846154e+01  7.27272727e+01  4.73684211e+01
 -3.92857143e+01  1.76470588e+01  2.00000000e+01  6.50406504e+00
  2.29007634e+00  6.71641791e+00 -1.18881119e+01  8.73015873e+00
  1.02189781e+01  3.44370861e+01  1.47783251e+00 -7.28155340e+00
  2.19895288e+01  2.40343348e+01 -1.31578947e+01  1.43939394e+01
  1.98675497e+00  5.19480519e+00 -4.32098765e+00  9.03225806e+00
  3.96449704e+01 -8.47457627e-01 -1.28205128e+01  1.96078431e+00
  1.39423077e+01  3.57142857e+01 -4.21052632e+01  3.63636364e+01
  6.00000000e+01 -8.33333333e+00  1.36363636e+01 -2.00000000e+01
 -2.00000000e+01  6.25000000e+00  5.88235294e+00  1.11111111e+01
 -3.05796440e+00 -8.47457627e-01 -1.42450142e-01  4.94531621e+00
  2.35613956e+00  1.72642762e+00  4.17754569e+00  1.42021721e+00
 -1.11202636e+00  5.95585173e+00  1.96540881e+00  5.29247911e+00
  5.29100529e+00 -3.51758794e+00 -6.25000000e+00  2.50000000e+00
  1.19241192e+01  3.14769976e+00 -3.99061033e+00 -8.80195599e+00
  1.79624665e+01 -1.27272727e+01  6.49350649e+00 -2.13414634e+01
  1.55038760e+00  3.05343511e+00  8.88888889e+00 -6.12244898e+00
  1.01449275e+01 -6.57894737e-01 -9.93377483e+00  2.64705882e+01
  6.39534884e+00 -1.45833333e+01  1.95121951e+01  6.80272109e+00
 -4.45859873e+00 -4.00000000e+00  8.33333333e+00  2.56410256e+00
  2.50000000e+00 -6.70731707e+00  7.18954248e+00  4.87804878e+00
 -2.20000000e+01  2.05128205e+01  1.06382979e+01 -7.69230769e+00
 -8.33333333e+00  0.00000000e+00 -2.27272727e+00  1.51162791e+01
 -8.08080808e+00 -1.53846154e+01  1.03896104e+01  1.81208054e+01
 -5.68181818e-01  4.57142857e+00 -3.82513661e+00  3.97727273e+00
  1.09289617e+00 -1.13513514e+01  0.00000000e+00 -5.48780488e+00
  7.74193548e+00  5.38922156e+00 -3.84615385e-01 -1.54440154e+00
 -8.23529412e+00  7.97720798e+00  2.63852243e-01  4.73684211e+00
  8.79396985e+00  4.38799076e+00 -1.40486726e+01  2.39382239e+01
  5.91900312e+00 -2.85714286e+01  2.60000000e+01  3.17460317e+01
 -6.02409639e+00 -8.97435897e+00  1.83098592e+01  2.38095238e+01
 -1.34615385e+01  1.33333333e+01  9.80392157e+00  8.03571429e+00
 -1.91387560e+00 -1.36585366e+01  1.35593220e+01  2.23880597e+01
  8.13008130e-01  4.83870968e+00 -7.69230769e+00  8.75000000e+00
  5.74712644e+00  2.53623188e+00  8.48056537e+00 -1.66666667e+00
  6.77966102e+00 -1.58730159e+00 -1.61290323e+00  1.80327869e+01
  1.25000000e+01 -1.35802469e+01 -1.42857143e+01  3.83333333e+01
  9.63855422e+00  0.00000000e+00  0.00000000e+00 -2.50000000e+01
  5.18518519e+01  2.43902439e+00  2.14285714e+01 -7.84313725e+00
  1.91489362e+01  5.35714286e+00 -1.69491525e+00 -2.58620690e+01
  2.32558140e+00 -1.66666667e+01 -1.03703704e+01  1.40495868e+01
  1.30434783e+01 -1.92307692e+00 -6.53594771e+00  1.11888112e+01
  3.77358491e+00  1.21212121e+00  1.01796407e+01  7.60869565e+00
 -7.93650794e-01  8.80000000e+00  2.13235294e+01  1.33333333e+01
  4.11764706e+01  4.20454545e+01  1.36000000e+01  1.40845070e+00
 -5.64814815e+01  9.09574468e+01  8.91364903e+00  3.75000000e+01
 -5.45454545e+01  4.00000000e+01  7.14285714e+01 -4.16666667e+01
  2.85714286e+01  2.55555556e+02  5.00000000e+01  1.25000000e+01
  5.92592593e+01 -1.86046512e+01  1.25000000e+02  5.55555556e+01
  9.28571429e+01 -7.40740741e+00  4.40000000e+01 -2.77777778e+00
 -1.42857143e+01 -1.66666667e+01 -4.40000000e+01  1.78571429e+02
  1.79487179e+01  1.66666667e+00 -1.63934426e+00  1.00000000e+01
  4.39393939e+01  4.00000000e+01  1.42857143e+01 -7.89473684e+00
 -5.71428571e+00 -7.57575758e-01  5.64885496e+01 -4.87804878e-01
  3.44827586e+00 -2.00000000e+01  9.58333333e+01  1.27659574e+01
  2.83018868e+01  4.85294118e+01  2.37623762e+01 -4.80000000e+00
 -3.27731092e+01  7.25000000e+01  1.08695652e+01 -2.16216216e+00
 -5.52486188e+00 -8.18713450e+00  4.39490446e+01 -8.84955752e-01
  2.45535714e+01  5.01792115e+00  4.77815700e+00 -6.18892508e+00
  1.90972222e+01  0.00000000e+00  7.50000000e+00  2.55813953e+01
  1.11111111e+01 -1.66666667e+00 -5.08474576e+00  7.14285714e+00
  2.00000000e+01 -1.25000000e+01 -4.76190476e+00  6.00000000e+01
 -1.77083333e+01 -1.60000000e+01 -4.76190476e+00  3.00000000e+01
  0.00000000e+00  0.00000000e+00 -1.15384615e+01  8.69565217e+00
  2.80000000e+01 -2.18750000e+01  2.80000000e+01  2.81250000e+01
  1.66666667e+02  0.00000000e+00 -4.00000000e+01  1.00000000e+02
  1.00000000e+02  4.16666667e+01 -5.88235294e+00 -6.25000000e+01
  1.33333333e+02 -7.14285714e+00  1.50943396e+01 -6.55737705e+00
  1.57894737e+01  1.51515152e+00 -4.47761194e+00  6.25000000e+01
  2.21153846e+01  3.93700787e+00 -1.06060606e+01  6.44067797e+01
 -8.76288660e+00  6.29629630e+01 -2.27272727e+01 -4.41176471e+01
  7.36842105e+01  0.00000000e+00 -2.12121212e+01  1.92307692e+01
  3.22580645e+00  4.06250000e+01  1.33333333e+01 -1.17647059e+01
 -1.66666667e+01  1.40000000e+02  2.50000000e+01 -6.66666667e+00
  2.85714286e+01  3.33333333e+01  4.58333333e+01 -1.14285714e+01
 -6.45161290e+00 -2.06896552e+01  1.73913043e+01 -5.00000000e+01
  0.00000000e+00  2.00000000e+02 -2.50000000e+01 -4.44444444e+01
  0.00000000e+00  8.00000000e+01  3.33333333e+01  4.37500000e+01
  1.30434783e+01 -4.61538462e+01  3.57142857e+01  5.78947368e+01
 -3.33333333e+00 -6.89655172e+00 -1.48148148e+01  3.04347826e+01
 -4.33333333e+01 -1.76470588e+01 -7.14285714e+00  2.69230769e+01
 -2.27272727e+01  1.56862745e+01  6.27118644e+01  1.14583333e+01
  7.10280374e+01 -2.34972678e+01  4.21428571e+01 -5.02512563e-01
 -1.42857143e+01  1.11111111e+01 -1.50000000e+01 -2.35294118e+01
  7.69230769e+00  5.00000000e+01 -2.38095238e+01  5.62500000e+01
 -2.00000000e+01  2.00000000e+01 -4.16666667e+00 -2.50000000e+01
  6.66666667e+01  1.20000000e+02 -8.18181818e+01  1.50000000e+02
  1.40000000e+01 -1.75438596e+00  6.25000000e+00  8.40336134e+00
 -7.75193798e-01  2.96875000e+01  1.20481928e+01 -1.12903226e+01
 -2.24242424e+01  3.43750000e+01 -3.48837209e+00 -8.57142857e+01
 -2.50000000e+01  6.66666667e+01 -2.00000000e+01 -7.50000000e+01
 -1.00000000e+02 -7.69230769e+00 -8.33333333e+00  1.59090909e+01
  3.92156863e+00 -1.13207547e+01  6.38297872e+01  4.80519481e+01
  8.77192982e+00 -4.35483871e+01  8.42857143e+01  4.65116279e+00
  1.25000000e+01 -4.44444444e+01  8.00000000e+01  4.44444444e+01
 -7.69230769e+00  1.66666667e+01  5.00000000e+01  4.76190476e+00
 -4.09090909e+01  5.38461538e+01 -3.00000000e+01 -2.22222222e+01
 -7.14285714e+00  5.38461538e+01  0.00000000e+00  1.00000000e+02
  2.25000000e+01 -1.22448980e+01  1.86046512e+01 -5.88235294e+00
  2.08333333e+01 -1.72413793e+00  1.57142857e+01 -4.93827160e+00
  3.89610390e+00  1.00000000e+01  1.70454545e+01  3.68932039e+01
  9.92907801e+00 -1.35483871e+01 -1.19402985e+01  1.01694915e+01
  1.23076923e+01 -1.38140162e+00 -1.93599818e-01  3.57142857e+00
  4.89148397e+00  3.79161853e+00  7.60979559e+00  7.88038368e+00
  2.82426778e+00 -9.15564598e+00  1.64706980e+01  2.68407980e+00]
Predicted: [ 8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027
  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027
  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027
  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027
  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  9.95888159 10.34111291 10.72334423 11.10557555 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027
  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027
  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763
  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555
 11.48780687 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895
  9.57665027  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687
 11.87003819  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027
  9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819
  8.04772499  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159
 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499
  8.42995631  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291
 10.72334423 11.10557555 11.48780687 11.87003819  8.04772499  8.42995631
  8.81218763  9.19441895  9.57665027  9.95888159 10.34111291 10.72334423
 11.10557555 11.48780687 11.87003819  8.04772499]


Age Group: 7-8
Boys Growth:
Actual: [-2.14285714e+01 -1.09090909e+01  2.24489796e+01  0.00000000e+00
  3.66666667e+01  1.95121951e+01 -1.02040816e+01 -2.04545455e+01
 -2.85714286e+00  2.94117647e+00  1.57142857e+01 -4.77941176e+00
  1.06177606e+01 -1.12565445e+01 -2.65486726e+00  1.43434343e+01
  1.36042403e+01 -1.32192846e+00 -1.24507486e+01 -1.32313231e+01
  5.91286307e+00  5.87659158e+00 -1.74531351e+00  3.28947368e+00
  1.91082803e+00 -5.81250000e+00  1.14797611e+01  5.59523810e+00
  3.32581736e+00 -4.96453901e+00 -2.33639495e+01  3.19101124e+01
  5.96252129e+00 -3.83100609e+00  1.39612807e+01 -1.28716106e+01
  7.76152981e+00 -8.07237300e+00  1.20363361e+01 -1.32432432e+01
 -1.71728972e+01 -1.39633286e+01 -1.63934426e-01  5.19978106e+00
 -8.93719807e+00  1.06100796e+00  2.09973753e+00 -2.82776350e+00
  2.11640212e+00  6.73575130e+00 -3.64077670e+00 -8.56423174e+00
  3.58126722e+00  9.30851064e+00 -9.97566910e+00 -2.58064516e+01
 -3.47826087e+00 -1.26126126e+01 -1.03092784e+01 -1.14942529e+00
  2.90697674e+01  2.70270270e+00 -4.38596491e+00 -1.83486239e+01
  1.12359551e+01  1.41414141e+01 -3.15917375e+00 -2.50941029e-01
 -1.38364780e+00  3.82653061e+00  2.24815725e+01 -4.41323972e+00
 -1.02833158e+01  3.09941520e+01 -1.31250000e+01  1.16135663e+01
  4.41988950e+00 -1.86046512e+00  2.84360190e+00  5.99078341e+00
 -4.34782609e-01 -3.49344978e+00  6.78733032e+00 -1.10169492e+01
 -6.19047619e+00 -1.26903553e+01  3.72093023e+01  8.05084746e+00
  4.36060755e+00 -3.52112676e+00 -5.83941606e-01  3.91581008e+00
  2.87329251e+00  9.15750916e-01  0.00000000e+00 -2.40471869e+00
 -1.07856811e+01  1.45909328e+01  7.00318327e+00 -4.70085470e+00
 -3.73094170e+00  3.89416806e+00 -4.17862267e+00 -2.84484372e+00
 -5.39395107e+00 -1.26247200e+00 -6.18684265e-01 -1.79497821e+01
  1.95498230e+01 -3.02517453e+00 -6.17378049e+00 -1.52721365e+01
  6.39181847e-01  6.09717371e+00 -2.57407962e+00 -2.54992320e+00
 -5.98991173e-01 -5.55026958e+00 -1.18871726e+01  1.74542683e+01
  5.84036340e+00  7.85854617e-01 -8.57699805e+00  2.98507463e+00
  7.66045549e+00 -1.00000000e+01  7.26495726e+00  5.77689243e+00
 -1.18644068e+01 -3.84615385e+00  1.68888889e+01 -2.09125475e+00
 -2.09459459e+01 -1.70940171e+00 -8.69565217e-01  7.01754386e+00
 -1.14754098e+01  4.81481481e+01  1.37500000e+01  4.39560440e+00
 -3.84210526e+01  1.79487179e+01  1.30434783e+01 -1.52439024e+00
 -5.65015480e+00  7.46513536e+00  1.60305344e+00  4.95867769e+00
  7.22977810e+00  3.53805073e+00  1.67633785e+00 -1.35700698e+01
  1.66544387e+01  9.24528302e+00 -1.25549278e+00 -8.90019072e-01
  3.84862091e-01 -7.53993610e+00  1.72771251e+00  1.69157609e+01
 -6.97269030e-01 -1.07665301e+01 -2.05245902e+01  4.62046205e+00
  3.62776025e+00  2.11538462e+01 -6.34920635e+00  1.69491525e+00
 -1.66666667e+00  5.08474576e+00  3.22580645e+00  9.37500000e+00
 -2.42857143e+01 -1.41509434e+01 -1.09890110e+00 -1.44444444e+01
 -4.49120603e+00  1.52910227e+00 -5.18218623e-01  2.47436106e+00
  4.14614774e+00  2.22696766e+00 -5.96836765e-02 -1.82143924e+00
 -9.71715328e+00  8.97759811e+00  2.68933539e+00 -2.47371676e+00
 -7.22891566e+00  8.88585099e-01 -4.53929539e+00 -3.97444996e+00
 -3.25203252e+00 -3.13216196e+00  5.83596215e+00 -1.26676602e+01
  2.34641638e+01 -8.29302004e-01 -3.23943662e+00 -1.07714702e+01
  2.93637847e+00 -1.01426307e+01 -4.40917108e+00  7.38007380e+00
  1.54639175e+00  8.29103215e+00 -2.26562500e+01  1.49494949e+01
  1.12478032e+01  1.72205438e+01 -1.13402062e+01 -2.76162791e+00
 -2.24215247e+00 -1.98776758e+00 -7.80031201e-01 -1.05345912e+01
  4.21792619e+00 -2.71500843e+01  2.19907407e+01  5.88235294e+00
 -3.97236615e+00 -3.05755396e+00 -6.30797774e+00 -7.12871287e+00
 -7.88912580e+00 -2.31481481e+00 -4.97630332e+00 -7.48129676e+00
 -1.72506739e+01  9.44625407e+00 -1.48809524e+00 -8.49802372e+00
 -9.28725702e+00  1.90476190e+00 -6.77570093e+00  1.25313283e+00
  7.67326733e+00  3.44827586e+00 -6.88888889e+00 -1.74224344e+01
  2.80346821e+01  4.51467269e+00 -4.08684547e+00 -5.28184643e+00
  2.36644799e+00  2.28885328e+00  4.49765048e+00  5.86723769e+00
  5.74433657e+00 -1.51109411e+00 -1.91881919e+01  2.05960106e+01
  4.94220805e+00  2.03252033e+00  1.15537849e+01  4.28571429e+00
  8.56164384e+00 -6.62460568e+00 -1.01351351e+00 -3.41296928e-01
  9.58904110e+00 -1.31250000e+01  3.09352518e+01  7.41758242e+00
  9.49720670e+00  3.57142857e+00 -3.94088670e+00  2.44871795e+01
  4.42842430e+00 -1.08481262e+00 -1.59521436e+00  5.06585613e-01
 -7.56048387e+00  2.18102508e-01  4.13492927e+00  3.28947368e+00
  5.73248408e+00  6.62650602e+00  4.23728814e+00 -4.06504065e+00
 -9.03954802e+00 -3.41614907e+00  3.21543408e-01 -3.20512821e-01
  1.06109325e+01 -4.06976744e+00  3.55329949e+00  4.90196078e+00
  9.34579439e+00 -1.19658120e+01  2.71844660e+01 -9.16030534e+00
 -2.94117647e+00  8.65800866e+00 -1.39442231e+01  3.70370370e+00
  2.67857143e+00 -7.39495798e+00  2.35934664e+00  5.49645390e+00
  1.34453782e+00  1.32669983e+00 -2.78232406e+00 -2.52525253e+00
 -6.56303972e+00 -1.14602588e+01  1.52400835e+01 -2.71739130e+00
  2.93072824e+00  5.43572045e+00  2.13584288e+01  4.38300742e+00
  1.87338501e+01  2.91621328e+01  2.52737995e-01  3.86554622e+00
 -7.01860841e+01  1.15061058e+02  2.20189274e+01  2.00000000e+02
 -2.50000000e+01  5.55555556e+01 -1.00000000e+02  2.24489796e+01
  4.66666667e+01  2.27272727e+00 -8.88888889e+00 -1.58536585e+01
  2.60869565e+01  1.40229885e+02  5.78947368e+01  2.21212121e+01
  4.16873449e+01 -1.68126095e+01  1.00000000e+01 -2.02020202e+00
  1.03092784e+01 -8.41121495e+00  1.73469388e+01  6.08695652e+00
  4.09836066e+00  3.14960630e+00 -2.67175573e+01  1.04166667e+01
  2.64150943e+01 -3.35968379e+00 -1.22699387e+00  2.27743271e+01
  5.56492411e+00  1.59744409e-01  2.87081340e+00  5.58139535e+00
  8.07635830e+00 -2.05163043e+01  3.26495726e+01  6.31443299e+00
 -2.97397770e+00  2.29885057e+00  7.04119850e+01 -1.75824176e+00
  9.17225951e+00  3.42213115e+01  9.16030534e+00 -2.79720280e+00
 -3.22302158e+01  3.75796178e+01  1.23456790e+00 -9.11350456e+00
  1.54968095e+00  6.73249551e+00  6.72834315e-01  1.08604845e+01
  6.55614167e+00 -4.24328147e-01 -5.32670455e+00 -1.72543136e+01
  2.81958296e+01  1.87411598e+01  1.07692308e+01  1.85185185e+00
 -8.63636364e+00  1.79104478e+01  1.05485232e+01 -1.29770992e+01
  2.41228070e+01  3.18021201e+00 -2.43150685e+01  3.30316742e+01
  1.66666667e+01  2.72727273e+00 -2.47787611e+01  4.70588235e+00
 -6.74157303e+00  1.68674699e+01  7.21649485e+00  0.00000000e+00
 -1.34615385e+01 -3.66666667e+01  7.71929825e+01  1.18811881e+01
  2.70270270e+00 -6.71052632e+01  1.80000000e+02  8.57142857e+00
  4.34210526e+01  1.19266055e+01 -2.45901639e+00 -1.00840336e+01
 -1.49532710e+01  0.00000000e+00  2.63736264e+01 -1.37440758e+01
  6.41025641e+00  1.39414802e+01 -2.87009063e+00  3.12597201e+01
  3.79146919e+00 -3.65296804e+00 -8.64928910e+00 -1.23216602e+01
  2.04142012e+01 -2.70270270e+00 -1.04868914e+01 -2.46861925e+01
  0.00000000e+00  6.66666667e+00 -1.04166667e+00 -1.15789474e+01
  1.19047619e+01  4.25531915e+00  7.65306122e+00  7.10900474e+00
  1.32743363e+01 -1.97183099e+01 -6.14035088e+00  1.40186916e+01
  2.29508197e+01  2.93333333e+01  1.64948454e+01 -3.09734513e+00
 -7.76255708e+00 -2.17821782e+01  7.59493671e+00  1.17647059e+01
  1.30434783e+01 -7.69230769e+00  1.25000000e+01 -2.22222222e+01
  8.57142857e+01  1.53846154e+01 -1.33333333e+01  2.56410256e+00
  7.50000000e+00  2.09302326e+01  2.69230769e+01  1.12676056e+01
 -1.26582278e+00 -1.15384615e+01 -5.79710145e+00  7.23076923e+01
  1.07142857e+01 -2.66129032e+01  4.39560440e+00 -3.26315789e+01
  1.40625000e+01  6.84931507e+00 -3.87205387e+00 -7.35551664e+00
  5.48204159e+00  1.23655914e+01  3.76395534e+01  1.27462341e+01
  5.65262076e+00  3.89105058e+00 -2.15355805e+01  4.26014320e+01
  7.69874477e+00 -1.60194175e+01 -7.51445087e+00 -6.25000000e+00
  2.13333333e+01 -1.20879121e+01  2.25000000e+01 -6.63265306e+00
  1.91256831e+01 -2.75229358e+01  9.49367089e+00 -1.38728324e+01
 -8.69565217e+00  9.52380952e+00 -8.69565217e+00  8.57142857e+01
  1.35897436e+02 -4.45652174e+01 -4.31372549e+01 -1.37931034e+01
 -3.60000000e+01  1.87500000e+01 -5.26315789e+00 -6.58082976e+00
 -1.04134763e+01 -1.36752137e+00  2.59965338e+00  4.52702703e+01
 -1.51162791e+00  1.41676505e+00 -5.58789290e+00 -3.46485820e+01
  1.84905660e+01  6.52866242e+00  5.55555556e+00 -2.10526316e+01
  1.33333333e+01  2.94117647e+01  1.81818182e+01  4.61538462e+01
 -2.89473684e+01 -1.48148148e+01 -2.17391304e+01 -3.33333333e+01
  2.50000000e+01 -1.20000000e+01 -2.94117647e+00  8.26446281e+00
  7.37913486e+00  9.47867299e+00  2.09956710e+01  1.52057245e+01
  9.62732919e+00 -3.92351275e+01  5.40792541e+01 -3.02571861e-01
  7.21649485e+00 -1.92307692e+00  1.66666667e+01 -1.68067227e+01
  2.52525253e+01  1.04838710e+01  1.89781022e+01 -2.57668712e+01
 -1.07438017e+01  6.01851852e+01  5.20231214e+00  6.58436214e+00
 -6.56370656e+00 -8.26446281e-01  3.33333333e+01  2.18750000e+01
  2.66666667e+01 -7.89473684e+00  0.00000000e+00 -2.08791209e+01
  1.11111111e+01  4.75000000e+00  5.12422360e+00]
Predicted: [2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617  3.32791212
 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146
 2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617  3.32791212
 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146
 2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617  3.32791212
 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146
 2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617  3.32791212
 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146
 2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  3.60546253 3.88301294 4.16056336 4.43811377 2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617  3.32791212
 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146
 2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617  3.32791212
 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146
 2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617
 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418
 4.9932146  2.21771046 2.49526088 2.77281129 3.0503617  3.32791212
 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146
 2.21771046 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253
 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046
 2.49526088 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294
 4.16056336 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088
 2.77281129 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336
 4.43811377 4.71566418 4.9932146  2.21771046 2.49526088 2.77281129
 3.0503617  3.32791212 3.60546253 3.88301294 4.16056336 4.43811377
 4.71566418 4.9932146  2.21771046]

Girls Growth:
Actual: [ 1.00000000e+02  0.00000000e+00  0.00000000e+00  7.50000000e+01
  1.42857143e+01  1.75000000e+02 -3.18181818e+01 -2.00000000e+01
  0.00000000e+00  8.33333333e+00 -2.30769231e+01 -1.03896104e+01
  3.62318841e+01 -9.57446809e+00  3.17647059e+01  1.25000000e+01
  2.38095238e+01  1.08974359e+01 -7.51445087e+00  6.25000000e+00
  1.76470588e+00 -1.32947977e+01  7.09219858e-01  5.63380282e+00
  8.00000000e+00  0.00000000e+00  4.25925926e+01  1.55844156e+01
  7.49063670e-01  1.59851301e+01 -2.94871795e+01  2.90909091e+01
  2.53521127e+01 -2.31023102e+00  6.75675676e-01 -6.71140940e+00
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  0.00000000e+00  4.16666667e+00  2.80000000e+01  3.75000000e+01
  1.81818182e+01  1.73076923e+01  3.27868852e+00  6.34920635e+00
 -1.49253731e+00  9.09090909e+00  2.77777778e+00 -2.14285714e+01
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  9.18918919e+00  7.69230769e+00 -3.57142857e+00 -1.48148148e+01
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 -9.45945946e+00 -6.21890547e+00  5.30503979e+00  2.34256927e+01
  1.14285714e+01  9.70695971e+00  9.18196995e+00  2.29357798e+00
 -1.94319880e+00  2.08841463e+01  2.52206810e+00 -5.17241379e+00
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  8.39971035e+00  4.67601870e+00 -6.19017230e+00 -1.56462585e+01
  2.60483871e+01  5.43825976e+00  3.35731415e+00 -7.42459397e+00
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  5.26315789e+00  6.25000000e+00 -2.28571429e+01  3.02832244e+01
  9.69899666e+00  5.00000000e+00  1.42857143e+01  2.08333333e+01
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  5.71428571e+01  1.81818182e+01 -3.84615385e+01  5.00000000e+01
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  1.10344828e+01  1.86335404e+01  1.57068063e+00 -1.34020619e+01
  2.38095238e+01  3.07692308e+01 -1.81818182e+00  1.48148148e+01
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  1.05769231e+01  2.30769231e+01  5.62500000e+01 -4.00000000e+00
  0.00000000e+00  1.66666667e+01 -1.42857143e+01 -1.25000000e+01
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 -4.87077535e+00 -9.40438871e-01  5.22151899e+00  5.46365915e+00
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  2.47933884e+00  5.64516129e+00  1.67938931e+01 -8.49673203e+00
  1.57142857e+01  3.02469136e+01 -3.69668246e+01  2.93233083e+01
  1.16279070e+01  1.29629630e+01  3.27868852e+00 -6.34920635e+00
  8.47457627e+00  3.35937500e+01 -1.16959064e+01  3.97350993e+00
 -8.91719745e+00 -1.25874126e+01  3.68000000e+01  2.33918129e+00
 -1.62162162e+01  8.60215054e+00 -1.38613861e+01  6.89655172e+00
  7.52688172e+00  3.00000000e+00  2.91262136e+00 -2.07547170e+01
 -9.52380952e+00  2.10526316e+01 -1.73913043e+01  0.00000000e+00
  1.31578947e+01 -5.81395349e-01 -1.16959064e+00  5.91715976e-01
  0.00000000e+00 -1.76470588e+00 -6.58682635e+00 -3.20512821e+00
  2.71523179e+01 -1.04166667e+00 -1.16373478e+01 -8.42266462e+00
  5.85284281e+00  9.63665087e+00  9.51008646e+00  7.63157895e+00
  6.11246944e+00  2.30414747e+00 -8.22072072e+00  2.58895706e+01
  1.16959064e+01 -1.26984127e+01  1.09090909e+01  1.31147541e+01
 -2.89855072e+00  1.34328358e+01  1.31578947e+01  0.00000000e+00
 -4.65116279e+00 -4.87804878e+00  2.43589744e+01  1.95876289e+01
  7.74193548e+00  1.25748503e+01  7.97872340e+00  2.31527094e+01
 -3.20000000e+00 -2.06611570e+00  1.35021097e+01 -4.83271375e+00
  1.56250000e+01  1.18243243e+01 -6.04229607e+00  0.00000000e+00
  8.16326531e+00  3.01886792e+01  2.89855072e+00  5.63380282e+00
 -2.53333333e+01  2.85714286e+01  1.25000000e+01  4.93827160e+00
  3.52941176e+00 -1.47727273e+01 -3.15789474e+01  3.84615385e+00
  2.96296296e+01  2.85714286e+01  1.77777778e+01  9.43396226e+00
 -2.24137931e+01 -2.22222222e+00  1.59090909e+01  2.35294118e+01
  7.93650794e+00 -1.18110236e+01  3.57142857e+00  4.31034483e+00
  3.14049587e+01  1.88679245e+00  1.35802469e+01 -7.06521739e+00
 -7.01754386e+00  1.06918239e+01  1.30681818e+01  1.60804020e+01
 -1.47826087e+01  1.02040816e+00  4.34343434e+01  4.36619718e+01
  2.64705882e+01  3.60465116e+01  1.33903134e+01  8.54271357e+00
 -7.50000000e+01  1.89814815e+02  1.50159744e+01 -6.00000000e+01
  5.00000000e+01 -1.66666667e+01  0.00000000e+00  1.11111111e+01
  1.40000000e+02 -1.66666667e+01  9.00000000e+01  1.00000000e+02
 -3.02631579e+01  1.75000000e+02  1.81818182e+01 -1.53846154e+01
  3.63636364e+01  9.33333333e+01  1.03448276e+01 -1.87500000e+01
  7.69230769e+00 -2.50000000e+01  4.28571429e+01 -5.33333333e+01
 -1.83333333e+01  4.08163265e+00  1.76470588e+01  9.16666667e+01
  1.21739130e+01 -1.00775194e+01  3.18965517e+01  5.22875817e+00
 -3.16770186e+01  6.36363636e+01  1.66666667e+01 -1.87500000e+01
  4.61538462e+01  1.63157895e+02  8.00000000e+00  1.11111111e+01
  7.83333333e+01 -9.34579439e-01  2.83018868e+00 -4.67889908e+01
  1.08620690e+02  1.48760331e+01 -1.14093960e+01  1.66666667e+01
  7.14285714e+00  7.27272727e+00  4.40677966e+01  1.49019608e+01
 -3.75426621e+00 -1.06382979e+00 -1.86379928e+01  3.87665198e+01
  1.90476190e+01  6.81818182e+00  6.38297872e+00  0.00000000e+00
  0.00000000e+00  6.00000000e+00 -1.69811321e+01  6.81818182e+01
  1.89189189e+01 -2.61363636e+01  2.61538462e+01  2.80487805e+01
 -1.57894737e+01  2.50000000e+01  1.50000000e+01 -2.17391304e+01
 -5.55555556e+00  6.47058824e+01 -1.07142857e+01  3.60000000e+01
 -4.11764706e+01  6.50000000e+01  9.09090909e+00 -5.71428571e+01
 -5.00000000e+01  3.00000000e+02  2.25000000e+02  1.53846154e+01
 -2.00000000e+01  4.16666667e+01 -4.11764706e+01  3.00000000e+01
  0.00000000e+00  0.00000000e+00  1.62162162e+01  1.16279070e+01
  2.08333333e+01  8.79310345e+01 -2.75229358e+00  1.88679245e+00
  4.62962963e+00  5.30973451e+00  3.69747899e+01  4.29447853e+00
 -3.54838710e+01 -3.50000000e+01  4.61538462e+01 -5.26315789e+00
  3.33333333e+01  2.91666667e+01  6.45161290e+00 -2.42424242e+01
  2.80000000e+01  4.06250000e+01 -1.33333333e+01 -6.00000000e+01
 -3.33333333e+01  2.75000000e+02 -6.66666667e+00  1.71428571e+02
  7.89473684e+00 -4.87804878e+00 -1.28205128e+01 -5.29411765e+01
  7.50000000e+01  7.14285714e+00  2.00000000e+02 -6.66666667e+01
  3.00000000e+02 -5.00000000e+01  5.00000000e+01  1.33333333e+02
 -7.14285714e+00  2.50000000e+01  2.00000000e+01 -2.77777778e+01
  6.15384615e+01  4.76190476e+01  6.45161290e+00 -9.09090909e+00
 -3.33333333e+00 -4.13793103e+01  1.17647059e+01  3.68421053e+01
 -2.38095238e+00 -4.87804878e+00 -2.56410256e+00  2.89473684e+01
  8.57142857e+01  1.09890110e+01  4.05940594e+01  1.26760563e+01
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 -5.00000000e+01  1.80000000e+02 -5.71428571e+01  6.66666667e+01
  6.00000000e+01  1.25000000e+01  0.00000000e+00 -3.88888889e+01
  9.09090909e+01  1.42857143e+01  1.00000000e+02  6.66666667e+01
  0.00000000e+00 -5.00000000e+01  4.00000000e+01 -4.10958904e+00
  1.57142857e+01  1.23456790e+00  1.34146341e+01  5.05376344e+01
  1.14285714e+01 -6.41025641e+00  0.00000000e+00 -3.08219178e+01
  3.66336634e+01  5.07246377e+00 -2.50000000e+01  1.00000000e+02
  2.50000000e+01  8.00000000e+01 -5.71428571e+01 -3.33333333e+01
  1.00000000e+01  1.51515152e+01  1.31578947e+01 -4.65116279e+00
  3.17073171e+01  7.59259259e+01  1.57894737e+01  4.54545455e+00
 -4.52173913e+01  7.93650794e+01  7.07964602e+00 -7.14285714e+01
  3.00000000e+02  1.37500000e+02 -4.21052632e+01  1.81818182e+01
  4.61538462e+01 -3.68421053e+01 -8.33333333e+00 -3.63636364e+01
  1.85714286e+02 -1.00000000e+01  6.25000000e+00 -3.52941176e+01
  5.45454545e+01  9.41176471e+01  6.36363636e+01 -3.70370370e+00
 -1.53846154e+01  1.13636364e+01 -1.22448980e+01  1.16279070e+01
  4.79166667e+01 -2.93103448e+01  5.12195122e+01  6.45161290e+00
  3.48484848e+01  3.25842697e+01 -1.69491525e+00  3.44827586e+00
  8.33333333e-01 -2.64462810e+01  4.26966292e+01  7.08661417e+00
 -5.48581471e+00  1.46101740e-01  5.94164456e+00  9.08863295e+00
  1.14069314e+01  1.05170993e+01  4.66958710e+00 -2.67141585e-02
 -1.38950744e+01  2.58197993e+01  5.17964318e+00]
Predicted: [ 8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011
 13.8067675  14.9158339  16.0249003  17.13396671 18.24303311 19.35209951
  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011
 13.8067675  14.9158339  16.0249003  17.13396671 18.24303311 19.35209951
  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011
 13.8067675  14.9158339  16.0249003  17.13396671 18.24303311 19.35209951
  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011
 13.8067675  14.9158339  16.0249003  17.13396671 18.24303311 19.35209951
  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951 13.8067675  14.9158339  16.0249003  17.13396671  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011
 13.8067675  14.9158339  16.0249003  17.13396671 18.24303311 19.35209951
  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011
 13.8067675  14.9158339  16.0249003  17.13396671 18.24303311 19.35209951
  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347
 12.6977011  13.8067675  14.9158339  16.0249003  17.13396671 18.24303311
 19.35209951  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011
 13.8067675  14.9158339  16.0249003  17.13396671 18.24303311 19.35209951
  8.2614355   9.3705019  10.4795683  11.5886347  12.6977011  13.8067675
 14.9158339  16.0249003  17.13396671 18.24303311 19.35209951  8.2614355
  9.3705019  10.4795683  11.5886347  12.6977011  13.8067675  14.9158339
 16.0249003  17.13396671 18.24303311 19.35209951  8.2614355   9.3705019
 10.4795683  11.5886347  12.6977011  13.8067675  14.9158339  16.0249003
 17.13396671 18.24303311 19.35209951  8.2614355   9.3705019  10.4795683
 11.5886347  12.6977011  13.8067675  14.9158339  16.0249003  17.13396671
 18.24303311 19.35209951  8.2614355 ]


Age Group: 6&U
Boys Growth:
Actual: [-1.36363636e+01  4.21052632e+01  1.85185185e+01 -3.12500000e+00
  8.38709677e+01  1.40350877e+01 -2.76923077e+01 -1.91489362e+01
 -7.89473684e+00  4.00000000e+01 -2.24489796e+01 -2.79720280e+00
  2.82374101e+01 -2.21598878e+01 -8.64864865e+00  6.07495069e+01
  1.05521472e+01 -3.32963374e-01 -1.87082405e+01 -2.68493151e+01
  1.92883895e+01 -1.61695447e+01 -7.16981132e+00  1.19241192e+01
  1.69491525e+00 -4.04761905e+00  4.20595533e+01 -4.36681223e-01
  2.21929825e+01 -1.06963388e+01 -3.64951768e+01  4.21518987e+01
  1.98575245e+01 -9.87996307e+00  3.66290984e+01 -1.43607049e+01
 -5.82311734e+00 -1.37610414e+01  2.71159030e+01 -1.92111959e+01
 -9.81627297e+00 -2.02561118e+01  5.03649635e+00  8.61709521e+00
 -9.54446855e+00 -2.87769784e+00  4.44444444e+00  3.54609929e+00
 -1.00456621e+01  4.06091371e+00  4.87804878e+00 -1.53488372e+01
 -4.94505495e+00  1.47398844e+01 -1.33501259e+01 -4.64516129e+01
  8.43373494e+00 -3.33333333e+00 -3.79310345e+01  7.40740741e+00
 -1.72413793e+00  1.57894737e+01  2.12121212e+01 -3.37500000e+01
  2.07547170e+01  1.40625000e+01 -1.48619958e+00  2.80172414e+00
 -7.54716981e+00 -2.72108844e+00  1.27039627e+01 -5.89451913e+00
 -2.96703297e+00  1.48357871e+01 -1.99211045e+01  3.32512315e+01
  8.22550832e+00 -5.11811024e+00  7.46887967e+00 -1.46718147e+01
 -5.42986425e+00  9.56937799e-01 -1.46919431e+01 -2.77777778e+00
 -1.14285714e+00 -2.02312139e+01  5.57971014e+01 -9.30232558e+00
 -6.26556017e+00  4.51527224e+00  4.82846252e+00  1.09090909e+00
  4.35651479e+00  3.82995021e-01  1.71690195e+00 -9.00225056e-01
 -1.55185466e+01  2.94354839e+01 -6.09207338e+00 -8.39148327e+00
  4.90234375e+00  1.39638801e+00 -1.01909658e+01  3.39398896e+00
 -6.52560807e-01  2.05015924e+00  4.29100839e-01 -3.29772771e+01
  4.50014489e+01 -6.05515588e+00 -6.20557682e+00 -3.00204022e+00
 -6.91105769e-01  2.72314675e-01  6.51780326e+00  2.54957507e+00
  3.75690608e+00 -7.13525027e+00 -2.77236239e+01  3.70091234e+01
  9.26462073e-01 -4.52488688e-01  6.36363636e+00  1.28205128e+00
  5.90717300e+00  1.19521912e+00 -2.10629921e+01  2.86783042e+01
 -1.33720930e+01  1.11856823e+00  0.00000000e+00 -3.53982301e+00
 -2.96296296e+00  1.52671756e+00 -1.35338346e+01 -9.56521739e+00
 -6.73076923e+00  9.58762887e+01  2.63157895e+00 -6.66666667e+00
 -3.29670330e+01  1.80327869e+01  1.11111111e+01 -4.24870466e+00
  4.32900433e-01  1.61637931e+00  4.66595970e+00  8.20668693e+00
  6.74157303e+00 -2.98245614e+00  1.80831826e-01 -1.25451264e+01
  4.24148607e+01  1.06521739e+01  7.31528895e+00 -4.70347648e+00
 -2.57510730e+00 -3.01027900e+00  3.40651022e+00  8.71156662e+00
 -4.71380471e+00 -2.06360424e+01 -3.23241318e+01  3.23684211e+01
  1.68986083e+00 -1.76470588e+00 -4.79041916e+00  1.13207547e+01
 -1.75141243e+01  1.57534247e+01  1.77514793e+00  2.09302326e+01
 -3.70192308e+01 -4.96183206e+01  3.63636364e+01 -2.00000000e+01
 -2.76941601e+00  1.28482972e+00  3.57634113e+00  3.67419212e+00
 -2.27725591e-01  4.05135521e+00 -2.31697285e+00 -1.20701754e+00
 -1.00582469e+01  1.33786132e+01 -7.89913625e+00 -4.14473684e+00
  1.37268360e-01 -5.07196710e+00 -7.14801444e+00 -2.41057543e+00
  7.41035857e+00  6.75074184e+00 -2.77970813e-01 -2.91986063e+01
  3.82874016e+01  4.41281139e+00 -1.47500000e+01 -6.15835777e+00
  1.20312500e+01  1.01813110e+01  1.58227848e+01  3.06010929e+00
 -5.19618240e+00  9.73154362e+00 -4.67889908e+01  6.18773946e+01
  3.66863905e+00 -5.34591195e+00  1.66112957e+00 -3.75816993e+00
 -3.39558574e+00  8.96309315e+00 -3.54838710e+00 -6.18729097e+00
  1.96078431e+00 -3.33916084e+01  3.70078740e+01  7.66283525e-01
 -7.68245839e+00 -4.57697642e+00 -1.17732558e+01 -5.93080725e+00
 -1.03327496e+01  2.34375000e+00  3.24427481e+00 -2.03327172e+00
 -4.81132075e+01  7.12727273e+01  2.12314225e-01 -8.85245902e+00
 -2.69784173e+00 -3.51201479e+00  9.57854406e+00  2.44755245e+00
 -5.46075085e+00  9.02527076e-01  1.96779964e+00 -3.10526316e+01
  4.65648855e+01 -1.31944444e+01 -4.63841451e+00  2.65310635e-01
  2.09481808e+00  4.38444924e+00  7.90399338e+00  3.01054650e+00
  6.75725987e+00 -7.14908457e-01 -2.80997541e+01  3.43429409e+01
 -1.74545455e+00  1.21093750e+01  1.11498258e+01 -6.89655172e+00
  6.06060606e+00 -7.61904762e+00 -3.78006873e+00  3.92857143e+00
 -1.37457045e+00  0.00000000e+00  2.82229965e+01  2.71739130e-01
  2.45098039e-01  2.32273839e+00  4.18160096e+00  2.79816514e+01
  8.96057348e-01 -2.13143872e+00 -9.98185118e-01  0.00000000e+00
 -7.42438130e+00  3.26732673e+00 -5.75263663e+00  1.60771704e+01
  8.31024931e-01 -3.84615385e+00  6.00000000e+00 -6.19946092e+00
  6.60919540e+00 -2.15633423e+00  3.30578512e+00 -1.60000000e+00
  1.51761518e+01 -1.55294118e+01  0.00000000e+00 -2.09401709e+01
  1.18918919e+01  1.69082126e+01  8.26446281e+00 -4.19847328e+00
 -1.59362550e+01 -6.16113744e+00 -8.58585859e+00  1.82320442e+01
  4.67289720e-01  3.69357045e+00 -2.63852243e-01  1.12433862e+01
  4.51843044e+00 -9.89761092e+00 -8.58585859e+00 -2.76243094e+00
  4.11931818e+00 -1.56889495e+01  1.90938511e+01  1.35869565e-01
  1.05769231e+01  1.49068323e+00  1.39534884e+01  9.34479055e+00
  2.41650295e+01  4.34335443e+01 -1.98565913e+00 -1.23804164e+00
 -8.21082621e+01  2.11783439e+02  4.41266599e+01  6.00000000e+01
  6.25000000e+01  2.30769231e+01 -9.37500000e+01  5.76923077e+00
 -9.09090909e+00  6.00000000e+00 -4.90566038e+01  1.48148148e+01
  1.45161290e+02  8.15789474e+01  3.33333333e+01  3.26086957e+01
  1.47540984e+01 -3.92857143e+01  2.96875000e+01  8.43373494e+00
  1.77777778e+01 -1.03773585e+01 -2.10526316e+00  1.39784946e+01
 -2.83018868e+00 -2.03883495e+01 -4.26829268e+01  9.36170213e+01
 -5.49450549e+00  2.29166667e+00  2.40325866e+01 -7.38916256e+00
  3.19148936e+00  8.59106529e+00  3.16455696e+00  1.02760736e+01
 -4.31154381e+00 -3.70639535e+01  5.26558891e+01 -5.90015129e+00
 -1.54639175e+00  4.71204188e+00  1.62500000e+02  2.28571429e+00
  0.00000000e+00  3.20297952e+01 -7.61636107e+00 -1.64885496e+01
 -4.22303473e+01  4.93670886e+01 -4.23728814e+00  3.22997416e+00
 -1.25156446e-01 -1.87969925e+00 -3.70370370e+00  3.60742706e+01
  6.33528265e+00 -7.05774519e+00 -2.56410256e+00 -2.10526316e+01
  4.21794872e+01  1.06402164e+01  4.52991453e+01  7.05882353e+00
  9.34065934e+00  3.01507538e+00  9.75609756e-01 -1.15942029e+01
  6.39344262e+01  1.66666667e+01 -3.45714286e+01  6.15720524e+01
 -1.08108108e+00 -2.08333333e+01 -1.05263158e+01  2.94117647e+01
  1.02272727e+01  7.21649485e+00  7.69230769e+00 -2.76785714e+01
 -2.96296296e+01  1.31578947e+02 -7.57575758e+00  1.06382979e+01
 -6.53846154e+01  2.38888889e+02 -2.13114754e+01  1.20833333e+02
  9.43396226e+00 -1.89655172e+01 -7.44680851e+00 -2.98850575e+01
  3.44262295e+01  1.46341463e+01  9.64630225e+00  6.68621701e+01
 -2.63620387e+00 -1.51624549e+01  2.48936170e+01  2.72572402e+00
  8.29187396e-01 -1.38157895e+01 -1.69847328e+01  1.70114943e+01
 -7.26915521e+00 -7.22222222e+00 -1.61676647e+01 -7.14285714e-01
 -1.43884892e+00  2.04379562e+01 -8.48484848e+00  1.32450331e+00
 -1.24183007e+01 -2.23880597e+00  3.51145038e+01  1.12994350e+00
 -2.44604317e+01  9.52380952e+00  2.60869565e+01  6.89655172e+00
  3.93548387e+01  1.89814815e+01  2.72373541e+00 -6.43939394e+00
 -3.40080972e+01  3.92638037e+01 -1.76211454e+00  3.18181818e+01
 -2.41379310e+01  1.36363636e+01 -1.20000000e+01  1.50000000e+02
 -2.72727273e+01  2.75000000e+01 -1.17647059e+01 -3.55555556e+01
  2.06896552e+01  0.00000000e+00 -2.15686275e+01 -7.50000000e+00
 -1.62162162e+01  1.29032258e+02  4.78873239e+01 -9.52380952e+00
  3.57894737e+01 -3.33333333e+01 -6.04651163e+01  1.67647059e+02
 -4.06593407e+01 -4.90018149e+00 -1.85114504e+01 -1.07728337e+01
 -6.03674541e+00  1.02234637e+02 -3.31491713e+00  2.57142857e+00
  1.68523677e+01 -2.89630513e+01  4.98322148e+01 -4.47928331e+00
 -2.39263804e+01 -1.45161290e+01  2.83018868e+00 -9.17431193e-01
  9.25925926e+00  3.30508475e+01  1.91082803e+00  6.25000000e+00
 -4.94117647e+01  4.53488372e+01 -2.00000000e+01 -1.53846154e+01
  1.36363636e+01 -8.00000000e+00  9.56521739e+01  6.88888889e+01
 -3.68421053e+01 -5.00000000e+01 -4.58333333e+01 -3.07692308e+01
  1.11111111e+01 -1.00000000e+01 -2.30769231e+01 -1.10000000e+01
  1.20786517e+01 -1.07769424e+01  8.98876404e+01 -2.51479290e+00
 -1.48710167e+01 -1.40819964e+01 -4.75103734e+01  3.59683794e+01
  2.47093023e+01 -2.41379310e+01 -4.09090909e+01 -1.53846154e+01
  1.45454545e+02  3.33333333e+01 -2.77777778e+01 -1.15384615e+01
  0.00000000e+00 -3.04347826e+01  5.00000000e+01 -1.25000000e+01
 -1.09929078e+01 -8.76494024e+00  1.26637555e+01  2.32558140e+00
  2.23484848e+01  5.51083591e+01 -7.98403194e-01  1.75050302e+01
 -5.25684932e+01  4.33212996e+01  1.18387909e+01 -7.31707317e+00
  3.28947368e+01  1.98019802e+00 -2.13592233e+01  3.20987654e+01
  3.27102804e+01 -1.83098592e+01  3.44827586e+00 -3.58333333e+01
  5.19480519e+01  5.98290598e+00 -2.47232472e+01 -4.90196078e+00
  8.76288660e+00  6.16113744e+00  5.00000000e+01  4.28571429e+01
 -2.08333333e-01 -1.46137787e+01 -3.49633252e+01  1.24060150e+01
  9.36454849e+00  1.18518519e+01]
Predicted: [3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247
 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998
 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247
 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998
 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247
 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998
 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247
 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998
 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 4.94445872 5.13430497 5.32415122 5.51399748 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247
 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998
 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247
 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998
 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247
 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998
 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872
 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747
 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497
 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372
 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122
 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997
 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748
 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622
 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373
 5.89368998 3.99522747]

Girls Growth:
Actual: [ 1.00000000e+02  1.50000000e+02 -8.00000000e+01  3.00000000e+02
  2.50000000e+02 -2.85714286e+01 -4.00000000e+01 -1.66666667e+01
  6.00000000e+01  1.25000000e+01  5.55555556e+01 -1.62790698e+01
  1.44444444e+02 -3.86363636e+01  1.29629630e+01  4.75409836e+01
  3.00000000e+01  0.00000000e+00 -7.69230769e+00 -2.77777778e+01
  2.69230769e+01 -1.61616162e+01 -8.43373494e+00  2.76315789e+01
 -2.06185567e+00  1.89473684e+01  6.72566372e+01  6.34920635e+00
  2.98507463e+00 -2.89855072e+00 -3.08457711e+01  6.25899281e+01
  8.84955752e-01 -5.83333333e+00  1.94690265e+01 -2.03703704e+01
  2.13953488e+01  2.68199234e+00  4.25373134e+01 -1.54450262e+01
 -1.11455108e+01 -2.99651568e+01  1.99004975e+01  1.03734440e+01
 -1.59090909e+01  8.10810811e+00  2.00000000e+01 -2.08333333e+01
  8.15789474e+01 -5.79710145e+00 -6.15384615e+00 -1.47540984e+01
  1.73076923e+01  6.88524590e+01 -1.65048544e+01 -4.00000000e+01
  4.44444444e+01 -2.30769231e+01 -6.00000000e+01  1.00000000e+02
  0.00000000e+00  0.00000000e+00  2.50000000e+01  0.00000000e+00
  4.00000000e+01  3.57142857e+01  1.36363636e+01  1.20000000e+01
 -8.33333333e+00  3.24675325e+01  5.58823529e+01 -1.88679245e+01
  3.87596899e+00  2.83582090e+01 -2.96511628e+01  5.04132231e+01
 -3.29670330e+00 -1.66666667e+01  0.00000000e+00 -8.00000000e+00
  1.30434783e+01  3.46153846e+01 -3.42857143e+01  4.34782609e+00
 -8.33333333e+00  1.36363636e+01  3.60000000e+01 -5.88235294e+00
 -1.33790738e+01  6.73267327e+00  4.45269017e+00  2.02486679e+01
  1.65435746e+01  1.31812421e+01  6.71892497e-01  9.34371524e+00
 -1.92268566e+01  3.60201511e+01 -5.18518519e+00 -1.23778502e+01
  8.17843866e-01  8.70206490e+00  1.28900950e+00  4.95646350e+00
  2.42501595e+00  1.08411215e+01 -4.04721754e+00 -2.81195079e+01
  5.17522412e+01 -2.36305048e+00  0.00000000e+00  8.08314088e+00
  1.28205128e+00  1.32911392e+01  2.30912477e+01  2.42057489e+00
  1.31462334e+01 -4.96083551e+00 -2.55494505e+01  3.92988930e+01
 -6.75496689e+00  5.88235294e+00  1.58333333e+02  7.52688172e+00
  2.60000000e+01 -3.17460317e+00 -4.34426230e+01 -1.44927536e+00
 -4.41176471e+00  2.30769231e+01 -2.00000000e+01 -3.12500000e+00
  9.09090909e+00  3.33333333e+01 -1.25000000e+01  7.14285714e+00
 -3.33333333e+01  1.40000000e+02 -1.25000000e+01 -1.42857143e+01
  3.88888889e+01 -3.20000000e+01  3.52941176e+01 -6.41025641e+00
  6.84931507e+00 -7.69230769e+00  4.02777778e+01  4.95049505e+00
  2.35849057e+01 -1.52671756e+00  1.55038760e+01  6.71140940e+00
  3.58490566e+01  1.11111111e+01 -1.99095023e+01 -5.64971751e+00
 -4.79041916e+00  2.20125786e+01 -4.12371134e+00 -1.61290323e+00
  1.09289617e+01 -2.06896552e+01 -2.48447205e+01  2.14876033e+01
  1.36054422e+00 -2.70270270e+00  0.00000000e+00 -1.38888889e+01
 -2.25806452e+01  8.33333333e+00 -7.69230769e+00  3.33333333e+01
 -3.43750000e+01 -3.80952381e+01  1.53846154e+01  0.00000000e+00
 -6.67402096e+00  7.38770686e+00  4.34782609e+00  1.52953586e+01
  4.02561757e+00  6.77220756e+00  2.01812191e+00  5.36939847e+00
 -8.46743295e+00  1.36877355e+01 -4.30780560e+00 -1.07344633e+01
  1.07594937e+01  3.42857143e+00 -4.97237569e+00 -6.97674419e+00
  2.12500000e+01  1.52061856e+01 -4.25055928e+00 -2.45327103e+01
  4.55108359e+01 -1.17021277e+01 -8.06451613e+00 -6.43274854e+00
  7.50000000e+00  1.56976744e+01  3.26633166e+01  2.65151515e+01
 -9.58083832e+00  3.31125828e+00 -5.12820513e+01  8.55263158e+01
  9.57446809e+00 -9.15032680e+00  1.79856115e+01  0.00000000e+00
 -9.14634146e+00  2.08053691e+01  3.33333333e+00 -4.30107527e+00
  1.79775281e+01 -3.66666667e+01  5.63909774e+01 -1.15384615e+01
 -2.63157895e+01  8.73015873e+00  8.02919708e+00  1.55405405e+01
 -7.60233918e+00 -5.06329114e+00 -4.00000000e+00  3.47222222e+00
 -4.96644295e+01  4.66666667e+01  3.63636364e+00 -5.36585366e+00
  8.24742268e+00 -8.57142857e+00  2.13541667e+01  2.57510730e+00
 -5.43933054e+00  3.53982301e+00  1.15384615e+01 -3.52490421e+01
  5.38461538e+01 -1.07692308e+01 -4.63917526e+00  2.56756757e+00
  9.22266140e-01  2.31070496e+01  1.13467656e+01  3.52380952e+00
  1.24195032e+01  4.58265139e+00 -2.21439750e+01  3.43718593e+01
 -2.91697831e+00  0.00000000e+00  3.85964912e+01 -1.77215190e+01
  2.61538462e+01 -4.87804878e+00 -5.12820513e+00  3.51351351e+01
  1.30000000e+01 -1.94690265e+01  3.95604396e+01 -3.14960630e+00
 -4.67836257e+00  1.96319018e+01  7.17948718e+00  1.38755981e+01
  2.14285714e+01  1.34948097e+01 -2.13414634e+00  1.65109034e+01
 -4.27807487e+00 -7.26256983e+00 -1.29518072e+01  6.15384615e+00
  2.89855072e+00  2.81690141e+00  1.64383562e+01  8.23529412e+00
 -1.52173913e+01  1.79487179e+01 -3.26086957e+00  5.61797753e+00
  5.31914894e+00 -4.04040404e+00  0.00000000e+00 -1.33333333e+01
  1.53846154e+01  3.33333333e+01  1.00000000e+01 -1.96969697e+01
 -1.32075472e+01  1.08695652e+01  7.84313725e+00  3.63636364e+01
 -4.00000000e+00  6.09756098e+00  2.01149425e+01  2.15311005e+01
  5.11811024e+00  7.49063670e-01 -7.43494424e+00  5.62248996e+00
  8.36501901e+00 -1.92982456e+01  4.82608696e+01 -5.86510264e+00
  7.81250000e+00  1.73913043e+01  2.83950617e+01  2.88461538e+00
  8.69158879e+01  4.65000000e+01  4.09556314e+00  9.83606557e-01
 -7.88961039e+01  1.47692308e+02  4.96894410e+01  2.50000000e+01
 -6.00000000e+01  1.50000000e+02  0.00000000e+00  1.33333333e+02
  7.14285714e+01 -4.16666667e+01  2.14285714e+02  1.36363636e+01
  8.00000000e+00  2.22222222e+01  2.72727273e+01 -5.00000000e+01
  7.14285714e+01  5.83333333e+01  4.73684211e+01  0.00000000e+00
 -1.07142857e+01 -6.40000000e+01  6.66666667e+01 -6.66666667e+00
  1.50000000e+01  1.30434783e+01  5.12820513e+00  4.87804878e+01
  8.19672131e+00  3.63636364e+01 -3.33333333e+00 -1.55172414e+01
 -3.12925170e+01  7.32673267e+01 -1.31428571e+01 -2.42424242e+01
  8.00000000e+00  1.62962963e+02  1.69014085e+01 -1.92771084e+01
  7.61194030e+01 -3.05084746e+01  1.09756098e+01 -4.72527473e+01
  7.91666667e+01  2.44186047e+01  3.38983051e+00 -3.27868852e+00
 -2.54237288e+00  1.04347826e+01  4.72440945e+01  1.06951872e+01
 -1.15942029e+01  4.37158470e+00 -7.32984293e+00  4.29378531e+01
  1.58102767e+01 -2.70833333e+01  2.28571429e+01  2.09302326e+01
 -3.84615385e+00  2.60000000e+01 -1.11111111e+01  4.28571429e+01
  1.50000000e+01 -1.19565217e+01  4.07407407e+01 -1.05263158e+01
  0.00000000e+00 -5.00000000e+00  0.00000000e+00  1.05263158e+02
 -7.69230769e+00  1.11111111e+01 -2.75000000e+01 -6.89655172e+00
 -4.44444444e+01  1.00000000e+02 -2.66666667e+01 -1.00000000e+02
 -5.00000000e+01  1.80000000e+03 -1.57894737e+01 -1.25000000e+01
 -6.42857143e+01  0.00000000e+00 -6.00000000e+01  8.00000000e+02
  9.67741935e+00  4.70588235e+01  2.00000000e+01 -2.16666667e+01
  1.04255319e+02  3.12500000e+00  7.07070707e+00 -3.11320755e+01
  7.53424658e+01  1.48437500e+01 -1.63265306e+01 -3.52941176e+01
  2.72727273e+01  0.00000000e+00  5.71428571e+01  2.72727273e+01
  3.57142857e+00 -1.37931034e+01  1.60000000e+01  0.00000000e+00
  6.89655172e+00 -6.45161290e+00 -1.11111111e+01  1.00000000e+02
  6.87500000e+01  7.40740741e+00  3.10344828e+01  5.52631579e+01
 -2.20338983e+01 -4.34782609e+00 -2.72727273e+01  2.18750000e+01
 -2.82051282e+01  2.00000000e+02  1.00000000e+02 -1.66666667e+01
 -1.25000000e+01  4.28571429e+01  2.00000000e+01 -5.00000000e+01
  1.20000000e+02 -2.72727273e+01  5.00000000e+01  1.41666667e+02
  6.20689655e+01  4.68085106e+01  2.31884058e+01 -3.76470588e+01
 -7.73584906e+01  3.66666667e+02 -6.96428571e+01  4.11764706e+01
 -3.95833333e+01 -2.06896552e+01  4.34782609e+00  3.16666667e+02
 -2.80000000e+01  1.25000000e+01  4.32098765e+01 -4.82758621e+01
  4.83333333e+01  0.00000000e+00  0.00000000e+00  1.42857143e+01
  1.00000000e+02  6.25000000e+00 -4.70588235e+01  6.66666667e+01
  0.00000000e+00  4.66666667e+01 -5.00000000e+01  3.63636364e+01
  4.00000000e+01  1.80000000e+02  9.28571429e+01 -4.44444444e+01
 -3.33333333e+01 -8.00000000e+01 -1.12903226e+01  1.81818182e+00
  1.78571429e+01  1.21212121e+01  5.67567568e+01  1.20689655e+01
 -1.46153846e+01 -1.80180180e+01 -3.07692308e+01  3.96825397e+01
 -1.13636364e+01  1.00000000e+02 -7.14285714e+01  2.50000000e+01
 -6.00000000e+01 -7.50000000e+01  0.00000000e+00 -3.33333333e+00
  2.75862069e+01 -2.43243243e+01  4.64285714e+01  5.36585366e+01
  6.82539683e+01 -1.79245283e+01  8.04597701e+00 -4.57446809e+01
  1.96078431e+01  9.83606557e+00  2.00000000e+01 -5.83333333e+01
  1.00000000e+02 -3.00000000e+01  5.71428571e+01 -9.09090909e+00
  3.00000000e+01 -2.30769231e+01  4.00000000e+01  2.14285714e+01
  2.35294118e+01 -5.78947368e+01  1.00000000e+02 -1.87500000e+01
  1.00000000e+02  6.53846154e+01  6.27906977e+01 -2.28571429e+01
 -3.70370370e+00 -3.84615385e+01  4.68750000e+01  2.55319149e+01
  3.05555556e+01  4.25531915e+00  1.63265306e+01  7.01754386e+00
  6.55737705e+01  1.28712871e+01 -2.80701754e+01  1.34146341e+01
 -2.79569892e+01  2.98507463e+01  1.26436782e+01 -7.51031303e+00
  8.26446281e+00  3.61080819e+00  1.33200795e+01  1.34984520e+01
  7.95599200e+00  3.08262444e+00  1.61777923e+00 -2.19747528e+01
  3.36768343e+01 -2.82146161e+00]
Predicted: [ 9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809
 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348
  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809
 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348
  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809
 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348
  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809
 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348
  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348 17.02664066 18.44742322 19.86820579 21.28898835  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809
 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348
  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809
 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348
  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809
 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348
  9.92272784 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066
 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784
 11.3435104  12.76429297 14.18507553 15.60585809 17.02664066 18.44742322
 19.86820579 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104
 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579
 21.28898835 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297
 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835
 22.70977091 24.13055348  9.92272784 11.3435104  12.76429297 14.18507553
 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091
 24.13055348  9.92272784]


Age Group: Season_1_Compared
Boys Growth:
Actual: [2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2017. 2018. 2019. 2020.
 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011.]
Predicted: [2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521
 2015.98921821 2016.9883312  2017.98744419 2018.98655718 2019.98567018
 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222
 2014.99010521 2015.98921821 2016.9883312  2017.98744419 2018.98655718
 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923
 2013.99099222 2014.99010521 2015.98921821 2016.9883312  2017.98744419
 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623
 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312
 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324
 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821
 2016.9883312  2017.98744419 2018.98655718 2019.98567018 2020.98478317
 2010.99365324]

Girls Growth:
Actual: [2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2011. 2012. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2019.
 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.
 2015. 2016. 2017. 2018. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2011. 2012. 2016. 2017. 2018. 2020. 2011. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021.]
Predicted: [2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434
 2015.9870356  2016.52173685 2017.05643811 2017.59113936 2018.12584062
 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309
 2015.45233434 2015.9870356  2016.52173685 2017.05643811 2017.59113936
 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183
 2014.91763309 2015.45233434 2015.9870356  2016.52173685 2017.05643811
 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
 2014.38293183 2014.91763309 2015.45233434 2015.9870356  2016.52173685
 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356
 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188
 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434
 2015.9870356  2016.52173685 2017.05643811 2017.59113936 2018.12584062
 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309
 2015.45233434 2015.9870356  2016.52173685 2017.05643811 2017.59113936
 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183
 2014.91763309 2015.45233434 2015.9870356  2016.52173685 2017.05643811
 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
 2014.38293183 2014.91763309 2015.45233434 2015.9870356  2016.52173685
 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356
 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188
 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434
 2015.9870356  2016.52173685 2017.05643811 2017.59113936 2018.12584062
 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309
 2015.45233434 2015.9870356  2016.52173685 2017.05643811 2017.59113936
 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183
 2014.91763309 2015.45233434 2015.9870356  2016.52173685 2017.05643811
 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
 2014.38293183 2014.91763309 2015.45233434 2015.9870356  2016.52173685
 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356
 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188
 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434
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 2015.45233434 2015.9870356  2016.52173685 2017.05643811 2017.59113936
 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183
 2014.91763309 2015.45233434 2015.9870356  2016.52173685 2017.05643811
 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
 2014.38293183 2014.91763309 2015.45233434 2015.9870356  2016.52173685
 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356
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 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434
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 2014.91763309 2015.45233434 2015.9870356  2016.52173685 2017.05643811
 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
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 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
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 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434
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 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
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 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2015.9870356
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 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
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 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
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 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
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 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
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 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
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 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058
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 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932
 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356
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Age Group: Season_2_Compared
Boys Growth:
Actual: [2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2017. 2018. 2019. 2020. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012.]
Predicted: [2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
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 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
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 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
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 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2017. 2018. 2019. 2020. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
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 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
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 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
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 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012.]

Girls Growth:
Actual: [2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
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 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012.
 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015.
 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020.
 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022.
 2012. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013.
 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014.
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 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.
 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022.
 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2015.
 2016. 2017. 2018. 2019. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019.
 2020. 2021. 2022. 2012. 2016. 2017. 2018. 2020. 2021. 2012. 2013. 2014.
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 2016. 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016.
 2017. 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017.
 2018. 2019. 2020. 2021. 2022. 2012. 2013. 2014. 2015. 2016. 2017. 2018.
 2019. 2020. 2021. 2022.]
Predicted: [2014.32352108 2014.86053554 2015.39755    2015.93456445 2016.47157891
 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
 2019.69366566 2014.32352108 2014.86053554 2015.39755    2015.93456445
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 2019.1566512  2019.69366566 2014.32352108 2014.86053554 2015.39755
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 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
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 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
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 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
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 2018.08262228 2018.61963674 2019.1566512  2019.69366566 2014.32352108
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 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
 2014.32352108 2014.86053554 2015.39755    2015.93456445 2016.47157891
 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
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 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
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 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228
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 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
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 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
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 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
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 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
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 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
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 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
 2019.69366566 2014.32352108 2014.86053554 2015.39755    2015.93456445
 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
 2019.1566512  2019.69366566 2014.32352108 2014.86053554 2015.39755
 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228
 2018.61963674 2019.1566512  2019.69366566 2014.32352108 2014.86053554
 2015.39755    2015.93456445 2016.47157891 2017.00859337 2017.54560783
 2018.08262228 2018.61963674 2019.1566512  2019.69366566 2017.00859337
 2017.54560783 2018.08262228 2018.61963674 2019.69366566 2014.32352108
 2014.86053554 2015.39755    2015.93456445 2016.47157891 2017.00859337
 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
 2014.32352108 2014.86053554 2015.39755    2015.93456445 2016.47157891
 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
 2019.69366566 2014.32352108 2014.86053554 2015.39755    2015.93456445
 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
 2019.1566512  2019.69366566 2014.32352108 2014.86053554 2015.39755
 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228
 2018.61963674 2019.1566512  2019.69366566 2014.32352108 2014.86053554
 2015.39755    2015.93456445 2016.47157891 2017.00859337 2017.54560783
 2018.08262228 2018.61963674 2019.1566512  2019.69366566 2014.32352108
 2014.86053554 2015.39755    2015.93456445 2016.47157891 2017.00859337
 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
 2014.32352108 2014.86053554 2015.39755    2015.93456445 2016.47157891
 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
 2019.69366566 2014.32352108 2014.86053554 2015.39755    2015.93456445
 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
 2019.1566512  2019.69366566 2014.32352108 2014.86053554 2015.39755
 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228
 2018.61963674 2019.1566512  2019.69366566 2014.32352108 2014.86053554
 2015.39755    2015.93456445 2016.47157891 2017.00859337 2017.54560783
 2018.08262228 2018.61963674 2019.1566512  2019.69366566 2014.32352108
 2014.86053554 2015.39755    2015.93456445 2016.47157891 2017.00859337
 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
 2014.32352108 2014.86053554 2015.39755    2015.93456445 2016.47157891
 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
 2019.69366566 2014.32352108 2014.86053554 2015.39755    2015.93456445
 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
 2019.1566512  2019.69366566 2014.32352108 2014.86053554 2015.39755
 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228
 2018.61963674 2019.1566512  2019.69366566 2014.32352108 2014.86053554
 2015.39755    2015.93456445 2016.47157891 2017.00859337 2017.54560783
 2018.08262228 2018.61963674 2019.1566512  2019.69366566 2014.32352108
 2014.86053554 2015.39755    2015.93456445 2016.47157891 2017.00859337
 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
 2014.32352108 2014.86053554 2015.39755    2015.93456445 2016.47157891
 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512
 2019.69366566 2014.32352108 2014.86053554 2015.39755    2015.93456445
 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674
 2019.1566512  2019.69366566 2014.32352108 2014.86053554 2015.39755
 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228
 2018.61963674 2019.1566512  2019.69366566 2014.32352108 2014.86053554
 2015.39755    2015.93456445 2016.47157891 2017.00859337 2017.54560783
 2018.08262228 2018.61963674 2019.1566512  2019.69366566 2014.32352108
 2014.86053554 2015.39755    2015.93456445 2016.47157891 2017.00859337
 2017.54560783 2018.08262228 2018.61963674 2019.1566512  2019.69366566
 2014.32352108]


import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression

# Assuming boys_growth_state and girls_growth_state are your DataFrames

# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')

# Plot growth for each age group
for age_group in boys_growth_state.columns:
    # Exclude 'Season_Start', 'District', 'State' columns from comparison
    if age_group in ('Season_Start', 'District', 'State'):
        continue

    # Extract x and y values for regression
    x = boys_growth_state['Season_Start'].values.reshape(-1, 1)
    y_boys = boys_growth_state[age_group].values.reshape(-1, 1)
    y_girls = girls_growth_state[age_group].values.reshape(-1, 1)

    # Remove NaN, infinity, and very large values
    mask_boys = np.isfinite(y_boys) & (y_boys < 1e10)
    mask_girls = np.isfinite(y_girls) & (y_girls < 1e10)

    x = x[mask_boys & mask_girls].reshape(-1, 1)
    y_boys = y_boys[mask_boys & mask_girls].reshape(-1, 1)
    y_girls = y_girls[mask_boys & mask_girls].reshape(-1, 1)

    # Fit linear regression models
    model_boys = LinearRegression().fit(x, y_boys)
    model_girls = LinearRegression().fit(x, y_girls)

    # Predict y values using the models
    y_pred_boys = model_boys.predict(x)
    y_pred_girls = model_girls.predict(x)

    # Plot boys' and girls' growth for the age group
    plt.figure(figsize=(10, 6))
    plt.scatter(boys_growth_state['Season_Start'], boys_growth_state[age_group], label='Boys')
    plt.scatter(girls_growth_state['Season_Start'], girls_growth_state[age_group], label='Girls')
    plt.plot(x, y_pred_boys, label='Boys Regression Line', color='blue')
    plt.plot(x, y_pred_girls, label='Girls Regression Line', color='orange')
    plt.title(f'Growth Comparison - {age_group} with Regression Line')
    plt.xlabel('Season Start')
    plt.ylabel('Growth')
    plt.legend()
    plt.show()


from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
import numpy as np

# Assuming boys_growth_state and girls_growth_state are your DataFrames

# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')

# Iterate over each age group column
for age_group in boys_growth_state.columns:
    # Exclude 'Season_Start', 'District', 'State' columns from comparison
    if age_group in ('Season_Start', 'District', 'State'):
        continue

    # Extract x and y values for regression
    x = boys_growth_state['Season_Start'].values.reshape(-1, 1)
    y_boys = boys_growth_state[age_group].values.reshape(-1, 1)
    y_girls = girls_growth_state[age_group].values.reshape(-1, 1)

    # Remove NaN, infinity, and very large values
    mask_boys = np.isfinite(y_boys) & (y_boys < 1e10)
    mask_girls = np.isfinite(y_girls) & (y_girls < 1e10)

    x = x[mask_boys & mask_girls].reshape(-1, 1)
    y_boys = y_boys[mask_boys & mask_girls].reshape(-1, 1)
    y_girls = y_girls[mask_boys & mask_girls].reshape(-1, 1)

    # Check if there are any remaining infinite or very large values
    if not np.all(np.isfinite(y_boys)) or not np.all(np.isfinite(y_girls)):
        print(f"Skipping age group {age_group} due to remaining NaN, infinity, or very large values.")
        continue

    # Fit linear regression models
    model_boys = LinearRegression()
    model_boys.fit(x, y_boys)

    model_girls = LinearRegression()
    model_girls.fit(x, y_girls)

    # Predict y values using the models
    y_pred_boys = model_boys.predict(x)
    y_pred_girls = model_girls.predict(x)

    # Print boys' and girls' growth for the age group
    print(f'Age Group: {age_group}')
    print('Mean Squared Error (Boys):', mean_squared_error(y_boys, y_pred_boys))
    print('Mean Squared Error (Girls):', mean_squared_error(y_girls, y_pred_girls))
    print('\nBoys Regression Coefficients:', model_boys.coef_[0][0])
    print('Boys Regression Intercept:', model_boys.intercept_[0])
    print('\nGirls Regression Coefficients:', model_girls.coef_[0][0])
    print('Girls Regression Intercept:', model_girls.intercept_[0])
    print('\n')

Age Group: All_Ages
Mean Squared Error (Boys): 1935.5108987079927
Mean Squared Error (Girls): 291.8921433309042

Boys Regression Coefficients: 0.962229417301423
Boys Regression Intercept: -1937.0644368746555

Girls Regression Coefficients: 0.5068497754396911
Girls Regression Intercept: -1016.3802923862363


Age Group: 19&over
Mean Squared Error (Boys): 3273.0441993196255
Mean Squared Error (Girls): 1590.7846068528154

Boys Regression Coefficients: 1.2089725330490013
Boys Regression Intercept: -2431.8977161472535

Girls Regression Coefficients: 1.085173549505563
Girls Regression Intercept: -2180.183727496564


Age Group: 17-18
Mean Squared Error (Boys): 1460.9358319288135
Mean Squared Error (Girls): 3150.8090194803067

Boys Regression Coefficients: 1.322573754157026
Boys Regression Intercept: -2664.889912528053

Girls Regression Coefficients: 1.1721827939438667
Girls Regression Intercept: -2353.637395794237


Age Group: 15-16
Mean Squared Error (Boys): 246.39277275146654
Mean Squared Error (Girls): 2146.8717557357604

Boys Regression Coefficients: 0.5896026757289666
Boys Regression Intercept: -1186.923910825153

Girls Regression Coefficients: -0.7775902367102362
Girls Regression Intercept: 1579.4866293665268


Age Group: 13-14
Mean Squared Error (Boys): 371.08035537756626
Mean Squared Error (Girls): 2327.075650527173

Boys Regression Coefficients: 0.2279776032345581
Boys Regression Intercept: -456.9170446844913

Girls Regression Coefficients: 0.38144076433481106
Girls Regression Intercept: -759.3894796737953


Age Group: 11-12
Mean Squared Error (Boys): 2626.2245212957546
Mean Squared Error (Girls): 873.0659704095862

Boys Regression Coefficients: 0.5557501191071347
Boys Regression Intercept: -1116.4565358215239

Girls Regression Coefficients: 0.6412688672452647
Girls Regression Intercept: -1284.0159996362538


Age Group: 9-10
Mean Squared Error (Boys): 6884.389888402031
Mean Squared Error (Girls): 1123.3456536802173

Boys Regression Coefficients: 1.7881431346874501
Boys Regression Intercept: -3600.468839825688

Girls Regression Coefficients: 0.3822313197579687
Girls Regression Intercept: -761.0016903591751


Age Group: 7-8
Mean Squared Error (Boys): 520.8155876299404
Mean Squared Error (Girls): 1974.4234503874304

Boys Regression Coefficients: 0.27755041357364224
Boys Regression Intercept: -556.2137216484246

Girls Regression Coefficients: 1.1090664012619196
Girls Regression Intercept: -2223.180163843313


Age Group: 6&U
Mean Squared Error (Boys): 1033.1429128550478
Mean Squared Error (Girls): 9026.832813677196

Boys Regression Coefficients: 0.18984625058793886
Boys Regression Intercept: -377.975428712189

Girls Regression Coefficients: 1.420782564041547
Girls Regression Intercept: -2848.6917910137367


Age Group: Season_1_Compared
Mean Squared Error (Boys): 0.0466382629382092
Mean Squared Error (Girls): 7.171244436773641

Boys Regression Coefficients: 0.9991129925897381
Boys Regression Intercept: 0.7783121516142728

Girls Regression Coefficients: 0.5347012556456204
Girls Regression Intercept: 937.4946029607443


Age Group: Season_2_Compared
Mean Squared Error (Boys): 0.0
Mean Squared Error (Girls): 7.07793535067229

Boys Regression Coefficients: 0.9999999999999989
Boys Regression Intercept: 2.2737367544323206e-12

Girls Regression Coefficients: 0.5370144571863757
Girls Regression Intercept: 933.8504332242471


import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose

# Assuming boys_state and girls_state are your DataFrames

# Choose the appropriate frequency for the decomposition
# If 'Season_Start' contains only the year, freq should be 1
freq = 1

# Ensure the 'Season_Start' column is in datetime format for boys_state
boys_state['Season_Start'] = pd.to_datetime(boys_state['Season_Start'], format='%Y')
boys_state.set_index('Season_Start', inplace=True)

# Ensure the 'Season_Start' column is in datetime format for girls_state
girls_state['Season_Start'] = pd.to_datetime(girls_state['Season_Start'], format='%Y')
girls_state.set_index('Season_Start', inplace=True)

# Perform time series decomposition for boys_state
result_boys = seasonal_decompose(boys_state['All_Ages'], model='additive', period=freq)

# Plot the decomposition components for boys_state
plt.figure(figsize=(12, 8))
plt.subplot(4, 1, 1)
plt.plot(result_boys.observed, label='Observed (Boys)')
plt.legend(loc='upper left')

plt.subplot(4, 1, 2)
plt.plot(result_boys.trend, label='Trend (Boys)')
plt.legend(loc='upper left')

plt.subplot(4, 1, 3)
plt.plot(result_boys.seasonal, label='Seasonal (Boys)')
plt.legend(loc='upper left')

plt.subplot(4, 1, 4)
plt.plot(result_boys.resid, label='Residual (Boys)')
plt.legend(loc='upper left')

plt.tight_layout()
plt.show()

# Print the decomposition results for boys_state
print("Decomposition Results (Boys):")
print("Trend:")
print(result_boys.trend.dropna())
print("\nSeasonal:")
print(result_boys.seasonal.dropna())
print("\nResidual:")
print(result_boys.resid.dropna())

# Perform time series decomposition for girls_state
result_girls = seasonal_decompose(girls_state['All_Ages'], model='additive', period=freq)

# Plot the decomposition components for girls_state
plt.figure(figsize=(12, 8))
plt.subplot(4, 1, 1)
plt.plot(result_girls.observed, label='Observed (Girls)')
plt.legend(loc='upper left')

plt.subplot(4, 1, 2)
plt.plot(result_girls.trend, label='Trend (Girls)')
plt.legend(loc='upper left')

plt.subplot(4, 1, 3)
plt.plot(result_girls.seasonal, label='Seasonal (Girls)')
plt.legend(loc='upper left')

plt.subplot(4, 1, 4)
plt.plot(result_girls.resid, label='Residual (Girls)')
plt.legend(loc='upper left')

plt.tight_layout()
plt.show()

# Print the decomposition results for girls_state
print("\nDecomposition Results (Girls):")
print("Trend:")
print(result_girls.trend.dropna())
print("\nSeasonal:")
print(result_girls.seasonal.dropna())
print("\nResidual:")
print(result_girls.resid.dropna())

Decomposition Results (Boys):
Trend:
Season_Start
2011-01-01       987.0
2011-01-01     15781.0
2011-01-01     16625.0
2011-01-01     23927.0
2011-01-01      3208.0
                ...   
2022-01-01      6802.0
2022-01-01      2800.0
2022-01-01      4403.0
2022-01-01      9348.0
2022-01-01    464932.0
Name: trend, Length: 636, dtype: float64

Seasonal:
Season_Start
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
             ... 
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
Name: seasonal, Length: 636, dtype: float64

Residual:
Season_Start
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
             ... 
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
Name: resid, Length: 636, dtype: float64

Decomposition Results (Girls):
Trend:
Season_Start
2011-01-01       87.0
2011-01-01     1504.0
2011-01-01     1194.0
2011-01-01     2543.0
2011-01-01      249.0
               ...   
2022-01-01      852.0
2022-01-01      208.0
2022-01-01      430.0
2022-01-01     1200.0
2022-01-01    91254.0
Name: trend, Length: 636, dtype: float64

Seasonal:
Season_Start
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
             ... 
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
Name: seasonal, Length: 636, dtype: float64

Residual:
Season_Start
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
2011-01-01    0.0
             ... 
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
2022-01-01    0.0
Name: resid, Length: 636, dtype: float64

	District	District_Code
0	Atlantic	1
1	Central	2
2	Massachusetts	3
3	Michigan	4
4	Mid-American	5
5	Minnesota	6
6	New England	7
7	New York	8
8	Northern Plains	9
9	Pacific	10
10	Rocky Mountain	11
11	Southeastern	12
12	All	13

	Season_Start	District	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
0	2011.0	1	36178.0	11581.0	3264.0	4144.0	4229.0	4616.0	3903.0	2925.0	1516.0
1	2011.0	2	58750.0	13970.0	3443.0	4870.0	6372.0	7354.0	8066.0	7309.0	7366.0
2	2011.0	3	46788.0	4359.0	2529.0	4317.0	6451.0	7297.0	7527.0	7184.0	7124.0
3	2011.0	4	52944.0	22852.0	2841.0	3900.0	4880.0	5117.0	4910.0	4353.0	4091.0
4	2011.0	5	35412.0	9815.0	2580.0	3281.0	3854.0	4282.0	4132.0	4008.0	3460.0
5	2011.0	6	54951.0	8596.0	1963.0	3382.0	7227.0	8419.0	8527.0	8380.0	8457.0
6	2011.0	7	35343.0	5449.0	1827.0	3122.0	4481.0	5242.0	4897.0	4909.0	5416.0
7	2011.0	8	48205.0	13134.0	2710.0	4109.0	5382.0	6027.0	5887.0	5437.0	5519.0
8	2011.0	9	13701.0	3378.0	758.0	993.0	1428.0	1610.0	1811.0	1768.0	1955.0
9	2011.0	10	42540.0	24134.0	1958.0	2392.0	2890.0	3112.0	3016.0	2682.0	2356.0
10	2011.0	11	39815.0	17966.0	2711.0	3355.0	3672.0	3638.0	3426.0	3058.0	1989.0
11	2011.0	12	46551.0	20522.0	2807.0	3984.0	4101.0	4636.0	4279.0	3460.0	2762.0
12	2011.0	13	511178.0	155756.0	29391.0	41849.0	54967.0	61350.0	60381.0	55473.0	52011.0

	Season_Start	District	State	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
631	2022.0	12	12.90	7654.0	2836.0	350.0	569.0	737.0	902.0	969.0	780.0	511.0
632	2022.0	12	12.91	3008.0	1852.0	97.0	133.0	194.0	186.0	201.0	200.0	145.0
633	2022.0	12	12.92	4833.0	2081.0	275.0	375.0	382.0	404.0	440.0	490.0	386.0
634	2022.0	12	12.93	10548.0	4427.0	601.0	876.0	972.0	1051.0	1048.0	989.0	584.0
635	2022.0	13	13.00	556186.0	168276.0	30895.0	46836.0	59141.0	63490.0	65415.0	63313.0	58820.0

	Season_Start	District	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
0	2011.0	1	33393.0	10701.0	3045.0	3839.0	3891.0	4241.0	3582.0	2705.0	1389.0
1	2011.0	2	51890.0	12551.0	3063.0	4308.0	5601.0	6429.0	7109.0	6441.0	6388.0
2	2011.0	3	37224.0	3060.0	2044.0	3525.0	5202.0	5898.0	6056.0	5850.0	5589.0
3	2011.0	4	48324.0	21111.0	2590.0	3523.0	4461.0	4605.0	4440.0	3936.0	3658.0
4	2011.0	5	32539.0	9045.0	2396.0	3043.0	3577.0	3947.0	3786.0	3666.0	3079.0

	Season_Start	District	State	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
0	2011.0	1	1.1	987.0	308.0	126.0	144.0	109.0	111.0	97.0	70.0	22.0
1	2011.0	1	1.2	15781.0	6091.0	1488.0	1740.0	1688.0	1727.0	1387.0	1088.0	572.0
2	2011.0	1	1.3	16625.0	4302.0	1431.0	1955.0	2094.0	2403.0	2098.0	1547.0	795.0
3	2011.0	2	2.1	23927.0	6121.0	1577.0	2293.0	2558.0	2966.0	3453.0	2793.0	2166.0
4	2011.0	2	2.2	3208.0	958.0	154.0	236.0	276.0	347.0	362.0	414.0	461.0

Data Exploration¶

Data Cleaning¶

Data Analysis¶

Growth Calculations by District¶

Growth Calculations by State¶

	Season_Start	District	All_Ages	19&over	17-18	15-16	13-14	12-Nov	10-Sep	8-Jul	6&U
0	2011	Atlantic	36178	11581	3264	4144	4229	4616	3903	2925	1516
1	2011	Central	58750	13970	3443	4870	6372	7354	8066	7309	7366
2	2011	Massachusetts	46788	4359	2529	4317	6451	7297	7527	7184	7124
3	2011	Michigan	52944	22852	2841	3900	4880	5117	4910	4353	4091
4	2011	Mid-American	35412	9815	2580	3281	3854	4282	4132	4008	3460

	Season_Start	District	All_Ages	19&over	17-18	15-16	13-14	12-Nov	10-Sep	8-Jul	6&U
0	2011	Atlantic	2785	880	219	305	338	375	321	220	127
1	2011	Central	6860	1419	380	562	771	925	957	868	978
2	2011	Massachusetts	9564	1299	485	792	1249	1399	1471	1334	1535
3	2011	Michigan	4620	1741	251	377	419	512	470	417	433
4	2011	Mid-American	2873	770	184	238	277	335	346	342	381

	Season_Start	District	State	All_Ages	19&over	17-18	15-16	13-14	12-Nov	10-Sep	8-Jul	6&U
0	2011	Atlantic	DE	1074	369	135	150	110	115	100	72	23
1	2011	Atlantic	E PA	17285	6664	1622	1924	1875	1905	1515	1165	615
2	2011	Atlantic	NJ	17819	4548	1507	2070	2244	2596	2288	1688	878
3	2011	Central	IL	26470	6713	1745	2545	2846	3301	3818	3096	2406
4	2011	Central	IA	3457	1009	176	261	300	374	394	438	505

	Season_Start	District	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
count	156.00000	156.000000	156.000000	156.000000	156.000000	156.000000	156.000000	156.000000	156.000000	156.000000	156.000000
mean	2016.50000	7.000000	70600.461538	22935.461538	3996.166667	5842.269231	7534.820513	8046.051282	8024.076923	7248.076923	6827.371795
std	3.46317	3.753708	113192.764057	37505.166464	6401.052648	9361.134520	12072.153655	12897.002383	12870.998331	11665.633093	11055.000438
min	2011.00000	1.000000	10981.000000	2419.000000	535.000000	732.000000	1075.000000	1274.000000	1436.000000	1463.000000	785.000000
25%	2013.75000	4.000000	33591.750000	6708.750000	1777.750000	2727.750000	3553.000000	3632.250000	3526.750000	3018.750000	2346.250000
50%	2016.50000	7.000000	41267.000000	11945.500000	2356.000000	3319.500000	3963.500000	4160.500000	4130.500000	3716.000000	3472.000000
75%	2019.25000	10.000000	46338.000000	21158.500000	2632.250000	3891.500000	5499.250000	5960.500000	6072.000000	5285.250000	5414.500000
max	2022.00000	13.000000	485100.000000	162647.000000	28005.000000	41025.000000	50585.000000	54437.000000	54628.000000	50949.000000	49619.000000

	Season_Start	District	State	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
count	636.00000	636.000000	636.000000	636.000000	636.000000	636.000000	636.000000	636.000000	636.000000	636.000000	636.000000	636.000000
mean	2016.50000	8.037736	8.393585	17317.094340	5624.116352	970.616352	1426.559748	1847.827044	1980.191824	1979.572327	1794.974843	1693.235849
std	3.45477	3.804493	3.906673	62364.684175	20380.952585	3490.383500	5139.783536	6670.693158	7148.075764	7147.883248	6497.035939	6164.764226
min	2011.00000	1.000000	1.100000	12.000000	11.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	2013.75000	5.000000	5.300000	1695.000000	490.500000	84.000000	112.000000	134.750000	156.250000	160.000000	148.750000	133.750000
50%	2016.50000	9.000000	9.400000	4552.000000	1514.000000	249.500000	351.000000	473.500000	530.000000	529.500000	468.000000	435.500000
75%	2019.25000	11.000000	11.700000	12167.000000	3813.500000	645.500000	958.000000	1351.500000	1375.000000	1338.250000	1121.500000	898.750000
max	2022.00000	13.000000	13.000000	485100.000000	162647.000000	26733.000000	39738.000000	50585.000000	54437.000000	54628.000000	50949.000000	49619.000000

	Season_Start	District	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U	Season_1_Compared	Season_2_Compared
0	2012.0	1	0.973258	7.419867	-2.725780	-2.188070	2.981239	-3.442584	-2.847571	-3.475046	-5.471562	NaN	2012.0
1	2013.0	1	1.097337	-1.618095	-4.456448	-1.970706	2.994759	0.830281	5.775862	5.898123	19.268850	2012.0	2013.0
2	2014.0	1	-0.868341	0.123795	-1.201413	0.977995	1.526533	-0.726568	-3.205651	-3.182640	-8.876117	2013.0	2014.0
3	2015.0	1	-0.396544	1.969443	0.572246	-0.430455	-0.835322	1.244206	-4.771260	-4.482630	-5.816398	2014.0	2015.0
4	2016.0	1	2.929467	0.363762	-0.568990	2.864091	-0.890493	-4.120482	1.532567	13.179507	50.074405	2015.0	2016.0

	Season_Start	District	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U	Season_1_Compared	Season_2_Compared
0	2012.0	1	-2.585278	-2.386364	-9.132420	-3.934426	0.000000	7.466667	-9.034268	-2.272727	-10.236220	NaN	2012.0
1	2013.0	1	8.072245	4.190920	5.025126	-2.389078	20.710059	-4.714640	6.849315	15.348837	66.666667	2012.0	2013.0
2	2014.0	1	4.229195	0.223464	2.392344	27.272727	11.274510	-2.864583	13.141026	1.209677	-21.052632	2013.0	2014.0
3	2015.0	1	4.679319	-2.564103	-8.411215	15.659341	-10.792952	13.136729	19.546742	11.952191	18.666667	2014.0	2015.0
4	2016.0	1	5.345420	-0.572082	13.265306	-10.688836	-2.469136	5.924171	-4.502370	29.893238	64.606742	2015.0	2016.0

	Season_Start	District	Age_Group	Lower_Growth_Percentage
0	2012.0	1	All_Ages	3.558536
1	2020.0	1	All_Ages	1.913054
2	2012.0	2	All_Ages	2.731824
3	2013.0	2	All_Ages	2.588978
4	2020.0	2	All_Ages	0.721547

	Season_Start	State	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U	Season_1_Compared	Season_2_Compared
0	2012.0	1.1	-15.400203	-6.168831	-26.984127	-23.611111	-11.926606	-18.018018	-14.432990	-21.428571	-13.636364	NaN	2012.0
1	2013.0	1.1	1.077844	-5.536332	5.434783	-1.818182	16.666667	10.989011	-7.228916	-10.909091	42.105263	2012.0	2013.0
2	2014.0	1.1	4.502370	4.029304	10.309278	0.925926	0.000000	4.950495	-6.493506	22.448980	18.518519	2013.0	2014.0
3	2015.0	1.1	-1.587302	1.760563	-25.233645	1.834862	0.000000	0.000000	9.722222	0.000000	-3.125000	2014.0	2015.0
4	2016.0	1.1	2.073733	-5.882353	11.250000	-6.306306	4.464286	-17.924528	-1.265823	36.666667	83.870968	2015.0	2016.0

	Season_Start	State	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U	Season_1_Compared	Season_2_Compared
0	2012.0	1.1	-10.344828	-18.032787	-66.666667	0.000000	500.000000	25.000000	-33.333333	100.000000	100.0	2011.0	2012.0
1	2013.0	1.1	-8.974359	-18.000000	100.000000	-83.333333	0.000000	-40.000000	150.000000	0.000000	150.0	2012.0	2013.0
2	2014.0	1.1	12.676056	9.756098	0.000000	300.000000	16.666667	33.333333	80.000000	0.000000	-80.0	2013.0	2014.0
3	2015.0	1.1	1.250000	-8.888889	-83.333333	125.000000	-14.285714	75.000000	-33.333333	75.000000	300.0	2014.0	2015.0
4	2016.0	1.1	32.098765	-2.439024	700.000000	-22.222222	83.333333	42.857143	50.000000	14.285714	250.0	2015.0	2016.0

	State	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
0	1.1	-18.034448	-19.480519	-51.587302	-35.416667	-12.844037	-3.603604	-11.340206	15.714286	72.727273
1	1.2	-11.596223	-11.771466	-19.758065	-15.287356	-7.523697	-15.923567	-7.642394	-0.643382	-6.643357
2	1.3	0.000000	12.505811	-16.911251	-11.304348	-8.213945	-23.179359	-10.295520	20.620556	69.308176
3	2.1	-6.745518	-6.747264	24.857324	20.191888	18.803753	-8.226568	-23.718506	-31.185106	-27.839335
4	2.2	-1.527431	-22.442589	18.181818	30.084746	41.666667	16.426513	15.469613	-10.628019	-25.379610

	State	All_Ages	19&over	17-18	15-16	13-14	11-12	9-10	7-8	6&U
0	1.1	42.528736	-50.819672	-33.333333	66.666667	1400.000000	400.000000	533.333333	400.000000	1300.000000
1	1.2	26.861702	-2.443281	36.567164	34.239130	26.737968	29.775281	70.312500	94.805195	93.023256
2	1.3	88.107203	46.747967	109.210526	114.782609	72.666667	53.886010	78.421053	152.482270	174.698795
3	2.1	26.779394	36.148649	41.666667	35.714286	31.944444	25.671642	19.726027	10.231023	10.833333
4	2.2	83.534137	17.647059	-45.454545	44.000000	154.166667	100.000000	131.250000	208.333333	95.454545